Vertex AI release notes

Batch predictions for Llama models on Vertex AI (MaaS) is available in Preview.

Batch prediction support for Gemini

Batch prediction is available for Gemini in General Availability (GA). Available Gemini models include Gemini 1.0 Pro, Gemini 1.5 Pro, and Gemini 1.5 Flash. To get started with batch prediction, see Get batch predictions for Gemini.

PSC-I Egress is supported for Ray clusters Vertex AI. PSC-I is recommended for private connectivity since it reduces the chance of IP exhaustion, and allows for transitive peering. Check out Private Service Connect interface for Ray on Vertex AI. This feature is available in Preview.

You can now fine-tune the following models from the Cloud console:

• Gemma 2
• Llama 3.1
• Mistral
• Mixtral

The Whisper large v3 and Whisper large v3 turbo models have been added to Model Garden.

Colab Enterprise is now available in the following regions:

• Hamina, Finland (europe-north1)
• Milan, Italy (europe-west8)
• Tel Aviv, Israel (me-west1)
• Warsaw, Poland (europe-central2)

See Colab Enterprise locations.

The Vertex AI Gemini API SDK supports tokenization capabilities for local token counting and computation. This is a streamlined way to compute tokens locally, ensuring compatibility across different Gemini models and their tokenizers. Supported models include gemini-1.5-flash and gemini-1.5-pro . To learn more, see Count tokens.

Vector Search Private Service Connect automation

Deploying an index with Private Service Connect automation is generally available (GA). You can set up a service connection policy so that you don't have to manually create a compute address and forwarding rule after each index deployment.

For more information, see Set up Vector Search with Private Service Connect.

The AI assistant in Vertex AI Studio can help you refine and generate prompts. This feature is in Preview. To learn more, see Use AI-powered prompt writing tools.

Prompt Guard and Flux were added to Model Garden.

You can deploy Hugging Face models on Google Cloud that have text embedding inference enabled or pytorch inference enabled. For more information, see the Hugging Face model deployment in the console.

Gemini in Colab Enterprise, which is a product in the Gemini for Google Cloud portfolio, is generally available. Gemini in Colab Enterprise helps you write code by suggesting code as you type. You can also use the Help me code tool to generate code from a description of what you want.

Gemini in Colab Enterprise is available to try at no cost through December 31, 2024.

To learn how to enable and activate Gemini in Colab Enterprise features, see Set up Gemini in Colab Enterprise.

Grounding: Dynamic retrieval for grounded results (GA)

Dynamic retrieval lets you choose when to turn off grounding with Google Search. This is useful when a prompt doesn't require an answer grounded in Google Search, and the supported models can provide an answer based on their knowledge without grounding. Dynamic retrieval helps you manage latency, quality, and cost more effectively.

This feature is Generally Available. For more information, see Dynamic retrieval.

Prompt templates let you to test how different prompt formats perform with different sets of prompt data. This feature is in Preview. To learn more, see Use prompt templates.

M125 release

The M125 release of Vertex AI Workbench user-managed notebooks includes the following:

• Patched a vulnerability with adm and docker permissions when the instance's root access isn't enabled.

The M125 release of Vertex AI Workbench managed notebooks includes the following:

• Patched a vulnerability with adm and docker permissions when the instance's root access isn't enabled.

M125 release

The M125 release of Vertex AI Workbench instances includes the following:

• bigframes 1.9.0 is now available in all environments except TensorFlow.
• Fixed a regression introduced in M124 where Conda was getting downgraded to an older version.
• Patched a vulnerability with adm and docker permissions when the instance's root access isn't enabled.

Use Gemini to directly analyze YouTube videos and publicly available media (such as images, audio, and video) by using a link. This feature is in Public Preview.

The new API parameters audioTimestamp, responseLogprob, and logprobs are in Public Preview. For more information, see API reference.

Gemini 1.5 Pro and Gemini 1.5 Flash now support multimodal input with function calling. This feature is in Preview.

The Vertex AI prompt optimizer adapts your prompts using the optimal instructions and examples to elicit the best performance from your chosen model. This feature is available in Preview. To learn more, see Optimize prompts.

You can now use customer-managed encryption keys (CMEK) to protect notebooks in Colab Enterprise.

For more information, see Use customer-managed encryption keys.

Add label metadata to generateContent and streamGenerateContent API calls. For details, see Add labels to API calls.

To ensure that VM resources are available when your custom training and prediction jobs need them, you can now use Compute Engine reservations. Reservations provide a high level of assurance in obtaining capacity for Compute Engine resources. This feature is available in Preview for A2 and A3 machine series reservations.

For more information, see Use reservations with training and Use reservations with prediction.

To reduce the cost of running your training and prediction jobs, you can now use Spot VMs. Spot VMs are virtual machine (VM) instances that are excess Compute Engine capacity. Spot VMs have significant discounts, but Compute Engine might preemptively stop or delete Spot VMs to reclaim the capacity at any time. This feature is available in Preview.

For more information, see Use Spot VMs with training and Use Spot VMs with prediction.

Schedule Vertex AI custom training jobs based on resource availability. For details, see the Vertex AI documentation.

The ability to back up and restore data on a Vertex AI Workbench instance is now available in Preview. For more information, see Back up and restore an instance.

Ray cluster's autoscaling feature is now supported. See Scale Ray clusters on Vertex AI

Gen AI Evaluation Service is Generally Available. To learn more, see the Gen AI Evaluation Service overview.

M124 release

The M124 release of Vertex AI Workbench user-managed notebooks includes the following:

• Pytorch 2.3.0 with CUDA 12.1 and Python 3.10 user-managed notebooks instances are now available.
• Fixed a bug that prevented kernels from appearing when the Cloud Resource Manager API is turned off and Dataproc is enabled.

The notebook scheduler is now generally available. See Schedule a notebook run.

M124 release

The M124 release of Vertex AI Workbench managed notebooks includes the following:

• Fixed a bug that prevented kernels from appearing when the Cloud Resource Manager API is turned off and Dataproc is enabled.

M124 release

The M124 release of Vertex AI Workbench instances includes the following:

• Fixed a bug that prevented kernels from appearing when the Cloud Resource Manager API is turned off and Dataproc is enabled.
• Spark notebooks on Dataproc: The Serverless Spark runtime template creation screen now has an easy-to-use UI for configuring resource allocation, autoscaling, and GPU settings.

Vertex AI SDK for Python supports token listing and counting for prompts without the need to make API calls. This feature is available in (Preview). For details, see List and count tokens.

The Vertex AI Model Registry now offers Preview support for model copy across different projects. For information about how to copy your model projects and regions, see Copy models in Model Registry.

New Imagen on Vertex AI image generation model and features

The Imagen 3 image generation models (imagen-3.0-generate-001 and the low-latency version imagen-3.0-fast-generate-001) are Generally Available to approved users. These models offer the following additional features:

• Additional aspect ratios (1:1, 3:4, 4:3, 9:16, 16:9)
• Digital watermark (SynthID) enabled by default
• Watermark verification
• User-configurable safety features (safety setting, person/face setting)

For more information, see Model versions and Generate images using text prompts.

The following models have been added to Model Garden:

• Gemma 2 2B: A foundation LLM by Google Deepmind.
• Qwen2: An LLM series by Alibaba Cloud.
• Phi-3: An LLM series by Microsoft.

M123 release

The M123 release of Vertex AI Workbench managed notebooks includes the following:

• Fixed a bug that caused conflicting permissions with the Jupyter user and google-sudoers.
• Updated Nvidia drivers to version 550.90.07 to fix vulnerabilities.

M123 release

The M123 release of Vertex AI Workbench user-managed notebooks includes the following:

• Fixed a bug that caused conflicting permissions with the Jupyter user and google-sudoers.
• Fixed a bug for custom container instances using a disabled root.

M123 release

The M123 release of Vertex AI Workbench instances includes the following:

• Fixed a bug that caused conflicting permissions with the Jupyter user and google-sudoers.

The following models have been added to Model Garden:

• 36 Hugging Face embedding models with verified deployment settings such as BAAI/bge-m3 and intfloat/multilingual-e5-large-instruct.
• 35 Hugging Face PyTorch models with verified deployment settings such as stabilityai/stable-diffusion-2-1.

For more information, see the Hugging Face model deployment in the console.

Launched Hex-LLM for high-efficiency large language model serving. This performant TPU serving solution is based on XLA and optimized kernels to achieve high throughput and low latency.

Hex-LLM uses several parallelism strategies for multiple TPU chips, quantizations, dynamic LoRA, and more. Hex-LLM supports the following dense and sparse LLMs:

• Gemma 2B and 7B
• Gemma 2 9B and 27B
• Llama 2 7B, 13B and 70B
• Llama 3 8B and 70B
• Mistral 7B and Mixtral 8x7B

Context caching is available for Gemini 1.5 Pro. Use context caching to reduce the cost of requests that contain repeat content with high input token counts. For more information, see Context caching overview.

Controlled generation is available on Gemini 1.5 Pro and supports the JSON schema. For more information, see Control generated output.

M122 release

The M122 release of Vertex AI Workbench user-managed notebooks includes the following:

• Updated Nvidia drivers to version 550.90.07 to fix vulnerabilities.

M122 release

The M122 release of Vertex AI Workbench instances includes the following:

• Updated Nvidia drivers to version 550.90.07 to fix vulnerabilities.

You can now use customer-managed encryption keys (CMEK) to protect runtimes in Colab Enterprise. Using CMEK for notebook files isn't currently supported.

For more information, see Use customer-managed encryption keys for runtimes.

Gemini in Colab Enterprise, which is a product in the Gemini for Google Cloud portfolio, is available in Preview. Gemini in Colab Enterprise helps you write code by suggesting code as you type. You can also use the Help me code tool to generate code from a description of what you want.

To learn how to enable and activate Gemini in Colab Enterprise features, see Set up Gemini in Colab Enterprise.

The notebook scheduler is now available in Preview. You can schedule a notebook to run immediately one time, or on a recurring schedule.

For more information, see Schedule a notebook run.

Experiment in the Vertex AI Studio login-free

The Vertex AI Studio multi-model prompt designer can be accessed login-free. With this feature, prospective customers can use the Vertex AI Studio to test queries before deciding to sign up and create an account. To learn more about this experience, see Vertex AI Studio console experiences or to access the console directly go to Vertex AI Studio.

You can now create a Vertex AI Workbench instance based on a custom container. This feature is available in Preview. Only custom containers derived from the Google-provided base container are supported. For more information, see Create an instance using a custom container.

You can now use Workforce Identity Federation with Vertex AI Workbench instances in Preview. Workforce Identity Federation lets you create and manage Vertex AI Workbench instances with credentials provided by an external identity provider (IdP). For more information, see Create an instance with third party credentials.

Model Monitoring v2 is in Preview, which centralizes model monitoring configuration and visualization on a model version and enables monitoring models being served outside of Vertex AI. For more information, see Vertex AI Model Monitoring overview.

Gemini models support the frequencyPenalty and presencePenalty parameters. Use frequencyPenalty to control the probability of repeated text in a response. Use presencePenalty to control the probability of generating more diverse content. For more information, see Gemini model parameters.

The following models have been added to Model Garden:

• E5: A text embedding model series that can be served with a GPU or CPU.
• Instant ID: An identity preserving text-to-image generation model.
• Stable Diffusion XL lightning: A text-to-image generation model that is based on SDXL but requires fewer inference iterations.

To see a list of all available models, see Explore models in Model Garden.

M121 release

The M121 release of Vertex AI Workbench user-managed notebooks includes the following:

• Updated Nvidia drivers to 550.54.15 to fix an issue where Nvidia drivers failed to install on startup after Debian 11 images upgraded kernel to linux-image-5.10.0-29-cloud-amd64.
• The linux-headers-cloud-amd64 metapackage is now installed for faster driver recompiling on kernel upgrades.
• TensorFlow 2.6 CPU and GPU images are deprecated. There will be no further updates to these images in future releases.

The M121 release of Vertex AI Workbench managed notebooks includes the following:

• Updated the R CPU kernel from R 4.3 to R 4.4.

M121 release

The M121 release of Vertex AI Workbench instances includes the following:

• Updated Nvidia drivers to 550.54.15 to fix an issue where Nvidia drivers failed to install on startup after Debian 11 images upgraded kernel to linux-image-5.10.0-29-cloud-amd64.
• The linux-headers-cloud-amd64 metapackage is now installed for faster driver recompiling on kernel upgrades.

Grounding Gemini with Google Search is GA

The Gemini API Grounding with Google Search feature is available in GA. This is available for Gemini 1.0 Pro models. To learn more about model grounding, see Grounding with Google Search.

Batch prediction support for Gemini

Batch prediction is available for Gemini in preview. Available Gemini models include Gemini 1.0 Pro, Gemini 1.5 Pro, and Gemini 1.5 Flash. To get started with batch prediction, see Get batch predictions for Gemini.

New stable text embedding models

The following text embedding models are available GA:

• text-embedding-004
• text-multilingual-embedding-002

For details on how to use these models, see Get text embeddings.

Vertex AI custom training supports TPU v5e. For details, see Training with TPU accelerators.

M120 release

The M120 release of Vertex AI Workbench managed notebooks includes the following:

• Minor bug fixes for the libcurl package.

M120 release

The M120 release of Vertex AI Workbench user-managed notebooks includes the following:

• Upgraded TensorFlow 2.15 user-managed notebooks to TensorFlow 2.15.1.
• Minor bug fixes for the libcurl package.

M120 release

The M120 release of Vertex AI Workbench instances includes the following:

• Minor bug fixes for the libcurl package.

Meta's open weight Llama 3 model is available in the Vertex AI Model Garden.

Vertex AI Feature Store

The following features of Vertex AI Feature Store are now generally available (GA):

• Optimized online serving: Serve features at ultra-low latencies. For more information, see Optimized online serving.

• Search using embeddings: Perform vector similarity searches to retrieve semantically similar or related features for real-time serving. You can search using embeddings if your online store is configured to support embeddings. For more information, see Search using embeddings.

• Feature view sync: Refresh or synchronize the feature data in a feature view within an online store from the feature data source in BigQuery. For more information, see Sync feature data to online store.

New Imagen on Vertex AI image generation model and features

The 006 version of the Imagen 2 image generation model (imagegeneration@006) is now available. This model offers the following additional features:

• Additional aspect ratios (1:1, 3:4, 4:3, 9:16, 16:9)
• Digital watermark (SynthID) enabled by default
• Watermark verification*
• New user-configurable safety features (safety setting, person/face setting)

For more information, see Model versions and Generate images using text prompts.

* The seed field can't be used while digital watermark is enabled.

New Imagen on Vertex AI image editing model and features

The 006 version of the Imagen 2 image editing model (imagegeneration@006) is now available. This model offers the following additional features:

• Inpainting - Add or remove content from a masked area of an image
• Outpainting - Expand a masked area of an image
• Product image editing - Identify and maintain a primary product while changing the background or product position

For more information, see Model versions.

Grounding Gemini and Grounding with Google Search

The Gemini API now supports Grounding with Google Search in Preview. Currently available for Gemini 1.0 Pro models.

Generative AI on Vertex AI security control update

Security controls are available for the online prediction feature for Gemini 1.0 Pro and Gemini 1.0 Pro Vision.

New text embedding models

The following text embedding models are now in Preview.

• text-embedding-preview-0409
• text-multilingual-embedding-preview-0409

When evaluated using the MTEB benchmarks, these models produce better embeddings compared to previous versions. The new models also offer dynamic embedding sizes, which you can use to output smaller embedding dimensions, with minor performance loss, to save on computing and storage costs.

For details on how to use these models, refer to the public documentation and try out our Colab.

System instructions

System instructions are supported in Preview by the Gemini 1.0 Pro (stable version gemini-1.0-pro-002 only) and Gemini 1.5 Pro (Preview) multimodal models. Use system instructions to guide model behavior based on your specific needs and use cases. For more information, see System instructions examples.

Supervised Tuning for Gemini

Supervised tuning is available for the gemini-1.0-pro-002 model.

Online Evaluation Service

Generative AI evaluation supports online evaluation in addition to pipeline evaluation. The list of supported evaluation metrics has also expanded. See API reference and SDK reference.

Text translation

Translate text in Vertex AI Studio is available in Preview.

Model Garden supports all Text Generation Inference supported models in HuggingFace:

• Verified deployment settings for about 400 Hugging Face text generation models (including google/gemma-7b-it, meta-llama/Llama-2-7b-chat-hf, and mistralai/Mistral-7B-v0.1).
• Other Hugging Face text generation models have unverified deployment settings that are auto generated.

M118 release

The M118 release of Vertex AI Workbench user-managed notebooks includes the following:

• PyTorch 2.1.0 with CUDA 12.1 and Python 3.10 user-managed notebooks instances are now available.
• PyTorch 2.2.0 with CUDA 12.1 and Python 3.10 user-managed notebooks instances are now available.
• Updated Nvidia drivers of older user-managed notebooks images to R535.

The M118 release of Vertex AI Workbench managed notebooks includes the following:

• Updated Nvidia drivers to R535, which fixed a bug where the latest PyTorch 2.0 kernel didn't work due to outdated drivers.

M118 release

The M118 release of Vertex AI Workbench instances includes the following:

• Updated Nvidia drivers to R535.

Vertex AI Feature Store

The following features of Vertex AI Feature Store are now available in Preview:

• Integration of Vertex AI Feature Store with Dataplex: Online store instances, feature views, and feature groups are now automatically registered as data assets in Data Catalog, a Dataplex feature that catalogs metadata from these resources. You can use the metadata search capability of Dataplex to search for and view the metadata of these resources. For more information, see Search for resource metadata in Data Catalog.

• Service account configuration for feature views: You can configure a feature view to use a dedicated service account. By default, every feature view uses the service account configured for your project. For more information, see Configure the service account for a feature view.

• Multiple entity IDs for a feature view: While creating or updating a feature view, you can specify multiple entity ID columns. For more information, see Create a feature view.

Create an empty index with Vector Search

You can create an empty index in Vector Search for batch and for streaming. No embedding data is required at index creation time, which enables faster startup time. To learn more, see Manage indexes.

Vertex AI Prediction

You can now use A3 machine types to serve predictions.

Vector Search feature launch

Update streaming index metadata: With this launch, you can directly update restricts and numeric restricts of data points inside StreamUpdate indexes without the compaction cost of a full update. To learn more, see Update dynamic metadata.

M117 release

The M117 release of Vertex AI Workbench instances includes the following:

• Removed the Cloud Storage browser in the left side pane in favor of the existing Mount shared storage button.

VPC Service Controls has general availability support in Colab Enterprise.

For more information, see Use VPC Service Controls.

Structured logging support for Vertex AI custom training. For details, see Write code to return container logs.

Ground Multimodal Models

Model grounding for gemini-pro is available in Preview. Use grounding to connect the gemini-pro model to unstructured text data stores in Vertex AI Search. Grounding lets models access and use the information in the data repositories to generate more enhanced and nuanced responses. For more information, see Ground multimodal models.

Gemma open models are available

Gemma models, a family of lightweight, open models built from the same research and technology used to create the Gemini models, are available to run on your hardware, mobile devices, or hosted services. To learn more, see Use Gemma open models and the Gemma Model Garden card.

Multimodal embeddings video support is generally available

Embeddings for video data is now generally available (GA) using the multimodal embedding model (multimodalembedding). For more information, see the product documentation.

This features incurs pricing based on the mode you use. For more information, see pricing.

M116 release

The M116 release of Vertex AI Workbench user-managed notebooks includes the following:

• Updated custom container user-managed notebooks to use NVIDIA driver version 535.104.05.
• Fixed bugs in custom container user-managed notebooks where GPUs either wouldn't attach to the container properly, or detached after some time.

The M116 release of Vertex AI Workbench managed notebooks includes the following:

• Fixed a bug (present in versions M113 through M115) that prevented new local kernels from being usable.

The following models have been added to Model Garden:

• Stable Diffusion XL LCM: The Latent Consistency Model (LCM) enhances text-to-image generation in Latent Diffusion Models by enabling faster and high-quality image creation with fewer steps.
• LLaVA 1.5: Deploy LLaVA 1.5 models.
• PyTorch-ZipNeRF: The Pytorch-ZipNeRF model is a state-of-the-art implementation of the ZipNeRF algorithm in the Pytorch framework, designed for efficient and accurate 3D reconstruction from 2D images.
• LLaMA 2 (Quantized): A quantized version of Meta's Llama 2 models.
• WizardLM: WizardLM is a large language model (LLM) developed by Microsoft, fine-tuned on complex instructions by adapting the Evol-Instruct method.
• WizardCoder: WizardCoder is a large language model (LLM) developed by Microsoft, fine-tuned on complex instructions by adapting the Evol-Instruct method to the domain of code.
• AutoGluon: With AutoGluon you can train and deploy high-accuracy machine learning and deep learning models for tabular data.
• Lama (Large mask inpainting): Use Large Mask Inpainting with fast Fourier convolutions (FFCs), a high receptive field perceptual loss, and large training masks for resolution-robust image inpainting.

Query an index from the Vector Search console

Vector Search has launched an improved console experience for querying both private and public deployed indexes, now available in Preview. From the console, you can create an index and endpoint, deploy the index to the endpoint, and query the index for nearest neighbors. For more information, see Manage indexes.

Vertex Prediction

You can now customize more deployment parameters when uploading your models, such as shared memory allocation and custom startup and readiness probes. These parameters may be useful when deploying LLMs.

For more information, see Deploy generative AI models, Custom container requirements for prediction, and ModelContainerSpec.

M115 release

The M115 release of Vertex AI Workbench user-managed notebooks includes the following:

• Added support for TensorFlow 2.15 with Python 3.10 on Debian 11.
• Added support for TensorFlow 2.14 with Python 3.10 on Debian 11.

The M115 release of Vertex AI Workbench managed notebooks includes the following:

• Fixed the BigQuery connector within PySpark containers.

M115 release

The M115 release of Vertex AI Workbench instances includes the following:

• Added support for venv kernels.

Model tuning for the textembedding-gecko and textembedding-gecko-multilingual models is available in GA. You can use supervised fine-tuning to tune the textembedding-gecko model. For more information, see Tune text embeddings.

Vertex AI Prediction

You can now use Cloud TPU v5e to serve online predictions. For more information, see Use Cloud TPUs for online prediction.

M114 release

The M114 release of Vertex AI Workbench user-managed notebooks includes the following:

• Starting with this release, Python 3.7 is no longer available.
• Upgraded R to 4.3 on Debian 11 Python 3.10 instances.
• Upgraded JupyterLab to 3.6.6.

The M114 release of Vertex AI Workbench managed notebooks includes the following:

• Starting with this release, Python 3.7 is no longer available.
• Added new Dataproc extension for remote kernels.
• Upgraded JupyterLab to 3.6.6.
• Fixed an issue that sometimes prevented users from running or scheduling notebooks using a default kernel.

Vertex AI Gemini models

Vertex AI Gemini Pro and Gemini Pro Vision multimodal language models are available in Preview. For more information, see the following topics:

• Overview of multimodal models
• Multimodal prompt design
• Vertex AI Gemini API reference
• Gemini Python SDK reference
• Migrate from PaLM API to Gemini API

Imagen 2 General Availability

The 005 version of Imagen's image generation model (imagegeneration@005) is now generally available (GA) for image generation tasks. This model version is now the default for image generation tasks. For more information, see the product documentation.

For general information about Imagen models and versions, see Imagen model versions and lifecycle.

Text embedding model 003 (textembedding-gecko@003) available

The updated stable version of the text embedding foundation model, textembedding-gecko@003, is available. textembedding-gecko@003 features improved quality compared to the previous stable versions, textembedding-gecko@001 and textembedding-gecko@002. For more information on model versions, see Model versions and lifecycle.

Version @002 of the models for text, chat, code, and code chat are available. The @002 model versions include improved prompt responses. The @002 models are:

• text-bison@002
• chat-bison@002
• code-bison@002
• codechat-bison@002

To ensure that you always use the stable model version, specify the model identifier with the version number. For example, text-bison@002. For more information, see Model versions and lifecycle.

Version 2 of the stable version of the Codey code completion foundation model, named code-gecko@002, is available. code-gecko@002 features improved quality and reduced latency compared to the previous stable version, code-gecko@001. These improvements can lead to a higher rate of acceptance.

Grounding with Vertex AI Search

Model grounding is available in (Preview). Use grounding to connect the text-bison and chat-bison models to unstructured data stores in Vertex AI Search. Grounding lets models access and use the information in the data repositories to generate more enhanced and nuanced responses. For more information, see the Grounding Overview.

The Unicorn model size for PaLM 2 for Text is generally available (GA). The text-unicorn model provides improved response quality and reasoning capability compared to the text-bison model. For details, see Model information.

ComputeToken API now available in Preview

The ComputeToken API is now available in (Preview). You can use this API to get a list of tokens for a given prompt. A token is a way to represent a common sequence of characters found in a text input. To learn more, see Get a list of tokens.

Vertex AI Feature Store

The following features of the new and improved Vertex AI Feature Store are now generally available (GA):

• Feature Registry: Register your feature data sources in BigQuery by creating feature groups and features. For more information, see Create a feature group and Create a feature.

• Cloud Bigtable online serving: Serve features from one or more BigQuery data sources. You can set up Cloud Bigtable online serving by defining online serving clusters called online store instances and creating feature views within the online store instances.

Note that the following features of Vertex AI Feature Store are still in Preview:

• Serve features at ultra-low latencies with Optimized online serving.
• Sync data in a feature view within an online store.
• Retrieve vector embeddings for real-time serving.

For more information, see About Vertex AI Feature Store.

M113 release

The M113 release of Vertex AI Workbench instances includes the following:

• Added the Dataproc JupyterLab plugin to Vertex AI Workbench instances. To get started, see Create a Dataproc-enabled instance.
• When using an instance's Google Cloud CLI, gcloud config is preset with the following defaults:
  • project is set to your instance's project.
  • Your compute region is set to your instance's region.
  • Your Dataproc region is set to your instance's region.
• Fixed an issue that prevented Dataproc kernels from working.
• Fixed a CORS (cross-origin resource sharing) error.

M113 release

The M113 release of Vertex AI Workbench user-managed notebooks includes the following:

• Miscellaneous bug fixes and improvements in Python 3.10 notebooks.

Numerical filtering available in Vertex AI Vector Search

With Vector Search you can restrict results by "filtering" your index results. In addition to filtering by using categorical restrictions, you can now use numeric filtering. To learn more, see Filter vector matches.

Generative AI on Vertex AI

Security controls are available for additional Generative AI on Vertex AI features.

The following models have been added to Model Garden:

• ImageBind: Multimodal embedding model.
• Vicuna v1.5: LLM finetuned based on llama2.
• OWL-ViT v2: SoTA Open Vocabulary Object Detection model.
• DITO: SoTA Open Vocabulary Object Detection model.
• NLLB: Multi-language translation model.
• Mistral-7B: SoTA LLM at small size.
• BioGPT: LLM finetuned for biomedical domain.
• BiomedCILP: Multimodal foundational model finetuned for biomedical domain.

To see a list of all available models, see Explore models in Model Garden.

New Vertex AI Vector Search Console

Vector Search has launched a console experience in Google Cloud for creating and deploying indexes, now available in Preview. From the console, you can create indexes, and create public or VPC endpoints for your indexes, and deploy. For more information, see Manage indexes.

Colab Enterprise is now generally available (GA). Colab Enterprise combines the popular collaborative features of Colaboratory with the security and compliance capabilities of Google Cloud. Colab Enterprise includes:

• Sharing and collaborating functionality, with IAM access control.
• Google-managed compute and runtime provisioning, with configurable runtime templates.
• Integrations with Vertex AI and BigQuery.
• Inline code completion with Duet AI (Preview) assistance.
• End-user credential authentication for running your notebook code.
• Idle shutdown for runtimes (Experimental).

To get started, see Introduction to Colab Enterprise or create a notebook and start coding.

M112 release

The M112 release of Vertex AI Workbench user-managed notebooks includes the following:

• Miscellaneous bug fixes and improvements.

Ray on Vertex AI is now available in Preview

Ray is an open-source framework for scaling AI and Python applications. Ray provides the infrastructure to perform distributed computing and parallel processing for your machine learning workflow.

You can now create Ray clusters and develop your Ray applications on Vertex AI. This feature is in Preview. For more information, see Ray on Vertex AI overview.

Model tuning for the textembedding-gecko model is now available in Preview

You can now use supervised fine-tuning to tune the textembedding-gecko model. This feature is in (Preview). For more information, see Tune text embeddings.

Vertex AI Prediction

You can now use C3 machine types to serve predictions.

Vertex AI Feature Store

The new and improved Vertex AI Feature Store is now available in Preview. With the new Vertex AI Feature Store you can streamline your feature management in the following ways:

• Store and maintain your offline feature data in BigQuery, taking advantage of the data management capabilities of BigQuery. In the new Vertex AI Feature Store, BigQuery serves as the offline store. You don't need to copy or import feature data to an offline store in Vertex AI.

• Register your feature data sources in BigQuery by creating feature groups and features.

• Define online serving clusters called online store instances; and then serve features from one or more BigQuery data sources, by aggregating them in a feature view within an online store instance. Use Optimized online serving for ultra-low latency needs and Cloud Bigtable online serving for high data volumes.

• Retrieve vector embeddings stored in BigQuery for real-time serving.

For more information, see About Vertex AI Feature Store.

Vertex AI Workbench instances are now generally available (GA). Vertex AI Workbench instances combine features from managed notebooks and user-managed notebooks to provide a robust data science solution. Supported features include:

• Idle timeout
• BigQuery and Cloud Storage integrations
• End-user and service account authentication
• VPC Service Controls
• Customer managed encryption keys (CMEK) and Cloud External Key Manager (Cloud EKM)
• Health status monitoring
• Scheduled notebook runs
• Dataproc integration

To get started, see Introduction to Vertex AI Workbench instances.

M111 release

The M111 release of Vertex AI Workbench instances includes the following:

• Miscellaneous software updates.

The M111 release of Vertex AI Workbench user-managed notebooks includes the following:

• PyTorch 2.0 user-managed notebooks instances now include PyTorch XLA 2.0.
• Miscellaneous software updates.

The M111 release of Vertex AI Workbench managed notebooks includes the following:

• Miscellaneous software updates.

Vertex AI Prediction

You can now use A2 Ultra machines to serve predictions in us-central1, us-east4, europe-west4, and asia-southeast1. Each A2 Ultra machine has a fixed number of NVIDIA A100 80GB GPUs attached.

Experiment management: Google Cloud console now supports visualization of your model's performance changes over steps during training, and shows advanced run comparisons. To learn more, see Compare and analyze runs: Google Cloud console.

Colab Enterprise is now available in Preview. Colab Enterprise combines the popular collaborative features of Colaboratory with the security and compliance capabilities of Google Cloud. Colab Enterprise includes:

• Sharing and collaborating functionality, with IAM access control.
• Google-managed compute and runtime provisioning, with configurable runtime templates.
• Integrations with Vertex AI and BigQuery.
• Inline code completion with Duet AI assistance.
• End-user credential authentication for running your notebook code.

To get started, see Introduction to Colab Enterprise or create a notebook and start coding.

Imagen on Vertex AI now offers the following Generally Available (GA) features:

• Subject model tuning (standard)*
• Style model tuning*

* Restricted access feature.

For more information about Imagen or how to get access to restricted GA features, see the Imagen on Vertex AI overview.

Stream responses from Generative AI models

Generative AI model streaming support is now Generally Available (GA). After you send a prompt, the model returns response tokens as they're generated instead of waiting for the entire output to be available.

Supported models are:

• text-bison
• chat-bison
• code-bison
• codechat-bison

To learn more, see Stream responses from Generative AI models.

New Generative AI support on Vertex AI models and expanded language support

Generative AI support on Vertex AI has been updated to include new language model candidates (latest models), language models that support input and output tokens up to 32k, and more supported languages.

For details, see Available models and Model versions and lifecycle.

Model tuning for the text-bison model is now Generally Available (GA)

Tuning the text-bison model with supervised fine-tuning (SFT) is now Generally Available (GA) . For more information, see Tune text models.

Model tuning for the chat-bison model is now available in Preview

You can now use supervised fine-tuning to tune the chat-bison model. This feature is in (Preview). For more information, see Tune text models.

New embedding model now available in Preview

Generative AI support on Vertex AI users can now create embeddings using a new model trained on a wide range of non-English languages in (Preview).

• textembedding-gecko-multilingual

To learn more, see Get text embeddings.

Reinforcement learning from human feedback (RLHF) tuning for text-bison The Generative AI text generation foundation model (text-bison) now supports RLHF tuning. The RLHF tuning feature is in (Preview). For more information, see Use RLHF model tuning.

Vertex AI Codey APIs language support

Vertex AI Codey APIs now support additional programming languages. For more information, see Supported coding languages

Vertex AI Codey APIs now support supervised fine-tuning (SFT)

The code chat (codechat-bison) and code generation (code-bison) Vertex AI Codey APIs models now support supervised fine-tuning (SFT). The supervised-fine tuning for Vertex AI Codey APIs models feature is in (Preview). For more information, see Tune code models.

Metrics-based model evaluation

You can evaluate the performance of foundation models and tuned models against an evaluation dataset for classification, summarization, question answering, and general text generation. This feature is available in (Preview).

To learn more, see Evaluate model performance.

Vertex AI Model Registry Models and managed datasets are now synced to Dataplex's Data Catalog service. Data Catalog enables organization-wide search and discovery of data artifacts, while still maintaining IAM boundaries. The sync and search of these assets is available in Preview. For more information, see Data Catalog documentation.

CountToken API now available in Preview

The CountToken API is now available in (Preview). You can use this API to get the token count and the number of billable characters for a prompt. To learn more, see Get token count.

The Vertex AI Pipelines Template Gallery is now generally available (GA). The Template Gallery contains Google-authored pipeline and component templates to bootstrap your MLOps practice. Customize and run the templates as-is or embed them into your own pipelines. For more information, see Use a prebuilt template from the Template Gallery.

Tabular Workflow for Forecasting is available in Preview. For documentation, refer to Tabular Workflow for Forecasting.

Vertex AI custom training has launched persistent resources in Preview. A persistent resource is a long-running cluster of machines that you can use to run custom training jobs. Once created, the persistent resource remains available for future training jobs, so you don't have to wait for compute resources to be provisioned each time you want to train a model.

The Vertex AI Matching Engine public endpoint is now generally available (GA). For information about how to get started, see Matching Engine Setup.

M110 release

The M110 release of Vertex AI Workbench user-managed notebooks includes the following:

• Added support for TensorFlow 2.13 with Python 3.10 on Debian 11.
• Added support for TensorFlow 2.8 with Python 3.10 on Debian 11.
• Miscellaneous software updates.

The M110 release of Vertex AI Workbench managed notebooks includes the following:

• Increased shared memory size to available memory capacity.
• Added support for Python 3.10 on Debian 11.
  • Added support for PyTorch 2.0 with Python 3.10.

Imagen Multimodal embeddings available in GA

Imagen on Vertex AI now offers the following GA feature:

• Multimodal embeddings

This feature incurs different pricing based on if you use image input or text input. For more information, see the multimodal embeddings feature page.

The schedules API for Vertex AI Pipelines is now generally available (GA). You can schedule recurring pipeline runs in Vertex AI by specifying a frequency in cron syntax, and optionally the start time and/or end time. Additionally, you can pause, resume, update, and delete schedules.For more information, see Schedule a pipeline run with scheduler API.

Vertex AI Workbench instances are now available in Preview. Vertex AI Workbench instances combine features from managed notebooks and user-managed notebooks to provide a robust data science solution. Supported features include:

• Idle timeout
• BigQuery and Cloud Storage integrations
• End-user and service account authentication
• VPC Service Controls
• Customer managed encryption keys (CMEK)
• Health status monitoring
• Run notebooks on a schedule
• Dataproc integration

To get started, see Introduction to Vertex AI Workbench instances.

Model tuning updates for text-bison:

• Upgraded tuning pipeline now offers more efficient tuning and better performance on text-bison.
• New learning_rate parameter lets you adjust the step size at each iteration.

For details, see Tune language foundation models.

Imagen on Vertex AI now offers the following Generally Available (GA) features:

• Image generation (text-to-image generation)*
• Image editing*
• Image visual captioning
• Visual Question Answering (VQA)

* Restricted access feature.

For more information about Imagen or how to get access to restricted GA or Preview features, see the Imagen on Vertex AI overview.

Imagen now supports human face generation for the following features:

• Image generation (text-to-image generation)*
• Image editing*

* Restricted access feature.

Human face generation is enabled by default, except for images with children and/or celebrities. For more information, see the usage guidelines.

The Vertex AI PaLM API has added support for the following languages:

• Spanish (es)
• Korean (ko)
• Hindi (hi)
• Chinese (zh)

For the complete list of supported languages, see Supported languages.

Support for batch text (text-bison) requests is now available in (GA). You can review pricing for the chat-bison model at Vertex AI pricing page.

Support for PaLM 2 for Chat (chat-bison) is now available in (GA). You can review pricing for the chat-bison model at Vertex AI pricing page.

Vertex AI model evaluation is now generally available (GA) with the following new Preview features:

• Model evaluation with sliced metrics.
• Model evaluation with fairness and bias metrics.
• Vision error analysis for AutoML image classification models.

Vertex Explainable AI

Support for example-based explanations is now generally available (GA).

Vertex AI Codey APIs

The Vertex AI Codey APIs are now generally available (GA). Use the Codey APIs to create solutions with code generation, code completion, and code chat. Because the Vertex AI Codey APIs are GA, you incur usage costs if you use them. To learn about pricing, see the Generative AI support on Vertex AI pricing page.

The models in this release include:

• code-bison (code generation)
• codechat-bison (multi-turn code chat)
• code-gecko (code completion)

The maximum tokens for input was increased from 4,096 to 6,144 tokens for code-bison and codechat-bison to allow longer prompts and chat history. The maximum tokens for output was increased from 1,024 to 2,048 for code-bison and codechat-bison to allow for longer responses.

Additional programming languages are supported. For more information, see Supported coding languages.

Several fine-tuning datasets were removed from the code-bison and codechat-bison models to implement the following improvements:

• Excessive chattiness.
• Artifacting, such as NBSP (non-breaking space) characters.
• Low quality code responses.

To learn about cloud horizontals, please see Vertex AI certifications.

Vertex AI Pipeline task-level logs are now generally available (GA) in Cloud Logging. Additionally, from Cloud Logging you can route pipeline logs to a Pub/Sub sink to power your event-driven architecture. For more information, see View pipeline job logs.

M109 release

The M109 release of Vertex AI Workbench user-managed notebooks includes the following:

• PyTorch 2.0 with Python 3.10 and CUDA 11.8 user-managed notebooks instances are now available.
• Miscellaneous software updates.

The M109 release of Vertex AI Workbench managed notebooks includes the following:

• Fixed a bug that caused high cpu utilization due to excessive internal diagnostic tool processes.
• Fixed a bug that was showing incorrect kernel image icons in the Jupyterlab launcher.

A100 80GB accelerators are now generally available (GA) for custom training jobs in the following regions:

• asia-southeast1
• europe-west4
• us-central1
• us-east4

For more information, see Locations.

The chat-bison@001 model has been updated to better follow instructions in the context field. For details, on how to create chat prompts for chat-bison@001, see Design chat prompts.

PaLM Text and Embeddings APIs, and Generative AI Studio

The Generative AI support on Vertex AI is now generally available (GA). With this feature launch, you can leverage the PaLM API to generate AI models that you can test, tune, and deploy in your AI-powered applications. With the GA of these features, you will incur usage costs if you use the text-bison and textembedding-gecko PaLM APIs. To learn about pricing, see the Vertex AI pricing page.

Features and models in this release include:

• PaLM 2 for Text: text-bison
• Embedding for Text: textembedding-gecko
• Generative AI Studio for Language

Vertex AI Model Garden

The Vertex AI Model Garden is now generally available (GA). The Model Garden is a platform that helps you discover, test, customize, and deploy Vertex AI and select OSS models. These models range from tunable to task-specific - all available on the Model Garden page in the Google Cloud console.

To get started, see Explore AI models and APIs in Model Garden.

Vertex AI Codey APIs

The Vertex AI Codey APIs are now in Preview. With the Codey API, code generation, code completion, and code chat APIs can be used from any Google Cloud project without allowlisting. The APIs can be accessed from the us-central1 region. The Codey APIs can be used in the Generative AI studio or programmatically in REST commands.

To get started, see the Code models overview.

Vertex Prediction

You can now specify a multi-region BigQuery table as the input or output to a batch prediction request.

Vertex Prediction

You can now co-host models on the same VM from the Google Cloud Console. Previously, this capability was available only from the REST API. For more information, see Share resources across deployments.

Vertex AI custom training now supports deep integration with Vertex AI Experiments. You can submit training jobs with autologging enabled to automatically log parameters and model performance metrics. For more information, see Run training job with experiment tracking

The scheduler API for Vertex AI Pipelines is now available in Preview. You can schedule recurring pipeline runs in Vertex AI by specifying a frequency, start time (optional), and end time (optional). For more information, see Schedule a pipeline run with scheduler API.

Generative AI Support for Vertex AI

Generative AI Support for Vertex AI is now available in Preview. With this feature launch, you can leverage the Vertex AI PaLM API to generate AI models that you can test, tune, and deploy in your AI-powered applications.

Features and models in this release include:

• PaLM 2 for Text: text-bison@001
• PaLM 2 for Chat: chat-bison@001
• Embedding for Text: textembedding-gecko@001
• Generative AI Studio for Language
• Tuning for PaLM 2
• Vertex AI SDK v1.25, which includes new features such as TextGenerationModel(text-bison@001), ChatModel(chat-bison@001), TextEmbeddingModel(textembedding-gecko@001)

You can interact with the generative AI features on Vertex AI by using Generative AI Studio in the Google Cloud console, the Vertex AI API, and the Vertex AI SDK for Python.

• Learn more about Generative AI Support for Vertex AI
• See an Introduction to Generative AI Studio
• Get started with a Generative AI Studio quickstart

Vertex AI Model Garden

The Vertex AI Model Garden is now available in Preview. The Model Garden is a platform that helps you discover, test, customize, and deploy Vertex AI and select OSS models. These models range from tunable to task-specific - all available on the Model Garden page in the Google Cloud console.

• To get started, see Explore AI models and APIs in Model Garden.

Vertex AI Prediction

You can now use G2 accelerator-optimized machine types to serve predictions. Each G2 machine has a fixed number of NVIDIA L4 GPUs attached.

M108 release

The M108 release of Vertex AI Workbench user-managed notebooks includes the following:

• Miscellaneous software updates.

Vertex AI Prediction

You can now update some scaling and container logging configuration settings on a DeployedModel without undeploying and redeploying it to an endpoint.

For more information, see update the scaling configuration and container logging.

M107 release

The M107 release of Vertex AI Workbench user-managed notebooks includes the following:

• Fixed a bug that displayed the wrong version of the JupyterLab user interface.
• Fixed a bug where a cron job for the diagnostic tool was added at every restart.
• Miscellaneous software updates.

M106 release

The M106 release of Vertex AI Workbench user-managed notebooks includes the following:

• Rolled back a previous change in which Jupyter dependencies were located in a separate Conda environment.
• Fixed a bug in which kernels used by notebooks did not contain the specified machine learning frameworks.
• Miscellaneous software updates.

The Vertex AI Matching Engine service now offers Preview support for deploying an index to a public endpoint. For information about how to get started, see Matching Engine Setup.

Vertex AI Prediction

You can now view logs for Vertex AI Batch Prediction jobs in Cloud Logging.

Vertex AI Pipelines is now integrated with Cloud Asset Inventory service. You can use Cloud Asset Inventory to search, export, monitor, and analyze pipeline resources and metadata, and also view the resource history.

The Vertex AI Model Registry now offers Preview support for model copy between regions. For information about how to copy your model between regions, see Copy models in Model Registry.

M105 release

The M105 release of Vertex AI Workbench user-managed notebooks includes the following:

• The following user-managed notebooks images are now available with Python 3.10 on Debian 11:

  • TensorFlow 2.11 CPU (tf-2-11-cpu-debian-11-py310)
  • TensorFlow 2.11 GPU with Cuda 11.3 (tf-2-11-cu113-notebooks-debian-11-py310)
  • PyTorch 1.13 with Cuda 11.3 (pytorch-1-13-cu113-notebooks-debian-11-py310)
  • Base CPU (common-cpu-notebooks-debian-11-py310)
  • Base GPU with Cuda 11.3 (common-cu113-notebooks-debian11-py310)

• The following user-managed notebooks images are now available with Python 3.9 on Debian 11:

  • TensorFlow 2.6 CPU (tf-2-6-cpu-notebooks-debian-11-py39)
  • TensorFlow 2.6 GPU with Cuda 11.3 (tf-2-6-cu113-notebooks-debian-11-py39)

• Jupyter-related libraries have been moved to a different Conda environment, separate from the one containing machine learning frameworks and base software libraries.

Vertex AI Pipelines cost showback with billing labels is now generally available (GA). You can now use billing labels to review the cost of a pipeline run, along with the cost of individual resources generated from Google Cloud Pipeline Components in the pipeline run. For more information, see Understand pipeline run costs.

M105 release

The M105 release of Vertex AI Workbench managed notebooks includes the following:

• Fixed an issue wherein a runtime with idle shutdown enabled doesn't detect activity and shuts down.
• Fixed an issue wherein the runtime data disk runs out of space and prevents access.
• Fixed an issue wherein end user credentials are not preserved after shutdown.
• Changed Health Agent logging levels from DEBUG to INFO.

Vertex AI supports running Explainable AI on certain types of BQML models when they are added to the Vertex AI Model Registry (GA). To learn more, see Explainable AI for BigQuery ML models.

Vertex AI Feature Store

The ability to delete feature values from an entity type is now generally available (GA). The following features are available:

• Delete feature values from specified entities
• Delete feature values from specified features within a time range

Links to additional resources:

• Learn more about the delete_feature_values method in the Vertex AI SDK for Python.
• View the Python code sample on GitHub
• View the Python (Async) code sample on GitHub

Vertex AI Prediction

You can now use N2, N2D, C2, and C2D machine types to serve predictions.

M104 release

The M104 release of Vertex AI Workbench user-managed notebooks includes the following:

• Fixed a regression in which jupyter-user metadata was ignored.
• Enabled access to the Jupyter Gateway Client configuration by using the notebook-enable-gateway-client and gateway-client-url metadata tags.
• Added the following packages:
  • google-cloud-artifact-registry
  • google-cloud-bigquery-storage
  • google-cloud-language
  • keyring
  • keyrings.google-artifactregistry-auth
• Fixed a bug in which curl could not find the right SSL certificate path by default.

M104 update

This update of the M104 release of Vertex AI Workbench managed notebooks includes the following:

• Fixed a bug where local and remote kernels are not displayed. This happens when remote kernels are not accessible.
• Minor bug fixes and improvements.

Vertex AI Prediction

Pre-built PyTorch containers for serving predictions from PyTorch models is generally available (GA).

Vertex AI Matching Engine now supports Private Service Connect in Preview. To learn how to set up a a Private Service Connect instance, see Using Private Service Connect.

Support for resource-level IAM policies for Vertex AI featurestore and entityType resources is generally available (GA). For more information, see Control access to resources.

M104 release

The M104 release of Vertex AI Workbench managed notebooks includes the following:

• Added a fix for a security vulnerability in single-user managed notebooks instances.
• Made enhancements to the network selection user experience in the managed notebooks executor.
• Minor bug fixes and improvements.

The Vertex AI Pipelines Template Gallery is now available in Preview. You can bootstrap your MLOps workflows with Google-authored pipeline and component templates. For more information, see Use a prebuilt template from the Template Gallery.

M103 release

The M103 release of Vertex AI Workbench user-managed notebooks includes the following:

• Fixed a bug in which a warning tells the user to run jupyter lab build when creating a new instance.
• Upgraded PyTorch to 1.13.1.
• Minor bug fixes and improvements.

Tabular Workflow for End-to-End AutoML is generally available (GA). For documentation, refer to Tabular Workflow for End-to-End AutoML.

Vertex AI TensorFlow Profiler

Vertex AI TensorFlow Profiler is generally available GA. You can use TensorFlow Profiler to debug model training performance for your custom training jobs.

For details, see Profile model training performance using Profiler.

Vertex AI Matching Engine

Vertex AI Matching Engine now offers General Availability support for updating your indices using Streaming Update, which is real-time indexing for the Approximate Nearest Neighbor (ANN) service.

Vertex AI Feature Store streaming ingestion is now generally available (GA).

M102 release

The M102 release of Vertex AI Workbench user-managed notebooks includes the following:

• TensorFlow 2.11 is now available.
• PyTorch 1.13 is now available.
• Regular security patches and package upgrades.

M101 release

The M101 release of Vertex AI Workbench includes the following:

• TensorFlow patch version upgrades:
  • From 2.8.3 to 2.8.4.
  • From 2.9.2 to 2.9.3.
  • From 2.10.0 to 2.10.1.
• TensorFlow 1.15 on Vertex AI Workbench is now deprecated.
• Added *.notebooks.cloud.google.com as part of the domains required for users to access Notebooks API. Removed *.datalab.cloud.google.com.
• Regular security patches and package upgrades.

The Pipeline Templates feature is now generally available (GA). The Your Templates tab is supported by Artifact Registry and allows you to publish and curate pipeline and component templatess. For documentation, refer to Create, upload, and use a pipeline template.

AutoML image model updates

AutoML image classification and object detection now support a higher-accuracy model type. This model is available in Preview.

For information about how to train a model using the higher accuracy model type, see Begin AutoML model training.

Batch prediction is currently not supported for this model type.

Cloud Logging for Vertex AI Pipelines is now generally available (GA). For more information, see View pipeline job logs.

Vertex AI Prediction

You can now perform some simple filtering and transformation on the batch input in your BatchPredictionJob requests without having to write any code in the prediction container. This feature is in Preview. For more information, see Filter and transform input data.

The Vertex AI Pipelines email notification component is now generally available (GA). This component enables you to configure your pipeline to send up to three emails upon success or failure of a pipeline run. For more information, see Configure email notifications and the Email notification component.

Vertex AI has added support for the following regions:

• us-west3 (Salt Lake City)
• europe-central2 (Warsaw)
• asia-southeast2 (Jakarta)
• me-west1 (Tel aviv)

Some features of Vertex AI are not supported in these regions. Check feature availability for all regions on the Vertex AI Locations page.

AutoML Image Classification Error Analysis

Error analysis allows you to examine error cases after training a model from within the model evaluation page. This feature is available in Preview.

For each image you can inspect similar images from the training set to help identify the following:

• Label inconsistencies between visually similar images
• Outliers if a test sample has no visually similar images in the training set

After fixing any data issues, you can retrain the model to improve model performance.

Feature Transform Engine is available in Preview. For documentation, refer to Feature engineering.

M100 release

The M100 release of Vertex AI Workbench includes the following:

• Fixed a bug that prevented an instance with a GPU from starting.
• Regular package updates.
• Miscellaneous bug and display fixes.

Vertex AI Prediction

You can now use A2 machine types to serve predictions.

Vertex ML Metadata

You can now filter contexts, executions, and artifacts by association and attribution.

Custom training on Vertex AI now supports NVIDIA A100 80GB GPUs on a2-ultragpu-1g/2g/4g/8g machines. For details, see Configure compute resources for custom training.

Vertex AI Prediction

Custom prediction routines (CPR) are now Generally Available. CPR lets you easily build custom containers for prediction with pre/post processing support.

Vertex AI Prediction

You can now use E2 machine types to serve predictions.

M98 release

The M98 release of Vertex AI Workbench managed notebooks includes the following:

• Upgraded Go from 1.16.5 to 1.19.2.
• Upgraded R from 4.1 to 4.2.
• Upgraded JupyterLab from 3.2 to 3.4.
• Miscellaneous bug and display fixes.
• Added a fix for the BigQuery SQL editor to run queries correctly in non-US locations.
• Regular package updates.

Learn more about managed notebooks versions.

Tabular Workflow for TabNet Training is available in Preview. For documentation, refer to Tabular Workflows for TabNet Training.

Tabular Workflow for Wide & Deep Training is available in Preview. For documentation, refer to Tabular Workflow for Wide & Deep Training.

Vertex AI Feature Store streaming ingestion is available in Preview.

The Vertex AI Model Registry is generally available (GA). Vertex AI Model Registry is a searchable repository where you can manage the lifecycle of your ML models. From the Vertex AI Model Registry, you can better organize your models, train new versions, and deploy directly to endpoints.

The Vertex AI Model Registry and BigQuery ML integration is generally available (GA). With this integration, BigQuery ML models can be managed alongside other ML models in Vertex AI to easily version, evaluate, and deploy for prediction.

Incrementally train an AutoML model

You can now incrementally train an AutoML image classification or object detection model by selecting a previously trained model. This feature is in Preview. For more information, see Train an AutoML image classification model.

Vertex AI Feature Store

The ability to delete feature values from an entity type is now available in Preview. The following features are available:

• Delete feature values from specified entities
• Delete feature values from specified features within a time range

Links to additional resources:

• Learn more about the delete_feature_values method in the Vertex AI SDK for Python.
• View the Python code sample on GitHub
• View the Python (Async) code sample on GitHub

Vertex AI model evaluation is now available in Preview. Model evaluation provides model evaluation metrics, such as precision and recall, to help you determine the performance of your models.

Vertex AI Model Monitoring

Vertex AI Model Monitoring now offers Preview support for batch prediction jobs. For more details, see Vertex AI Model Monitoring for batch predictions.

Vertex AI Feature Store

Feature value monitoring is now generally available (GA).

Vertex AI Matching Engine

Vertex AI Matching Engine now offers Preview support for updating your indices using Streaming Update, which is real-time indexing for the Approximate Nearest Neighbor (ANN) service.

The option to configure pipeline run caching (enable_caching) is now available in the Cloud console.

M96 release

The M96 release of Vertex AI Workbench managed notebooks includes the following:

• Fixed a problem where users were not able to save large Notebooks.
• Fixed a display issue when using JupyterLab's simple interface.
• Improved timeout behavior switch hardware operations.
• Improved error messaging when a service account cannot access the Runtime.
• Security fixes.
• Regular package refreshment and bug fixes.

Learn more about managed notebooks versions.

You can now limit the number of concurrent or parallel task runs in a pipeline run using dsl.ParallelFor. For more information, see the Kubeflow Pipelines SDK Documentation.

M95 release

The M95 release of Vertex AI Workbench managed notebooks includes the following:

• Fixed a bug where users were regularly getting a 502 error when trying to access JupyterLab.
• Fixed a bug where opening an instance in Single User mode slowed the start of an instance.
• Fixed a bug where a managed notebooks instance was not starting after adding a GPU.
• Fixed bugs on the Serverless Spark form input.
• Improved the ActivityLog refresh after Serverless Spark creation.
• Fixed a bug related to the display of materialized views in BigQuery.
• Refreshed the JupyterLab interface with an improved Google-specific theme.
• Fixed a bug related to viewing Cloud Storage buckets and folders with large numbers of objects.
• Regular package refreshment and bug fixes.

Learn more about managed notebooks versions.

Vertex Explainable AI

Vertex Explainable AI now offers Preview support for example-based explanations. For more information, see Configure example-based explanations for custom training.

TensorFlow Profiler integration: Debug model training performance for your custom training jobs. For details, see Profile model training performance using Profiler.

Vertex AI now offers Preview support for Custom prediction routines (CPR). CPR lets you easily build custom containers for prediction with pre/post processing support.

NFS support for custom training is GA. For details, see Mount an NFS share for custom training.

The Pipeline Templates feature is available in Preview. For documentation, refer to Create, upload, and use a pipeline template.

The features supported by pipeline templates include the following:

• Create a template registry using Artifact Registry (AR).
• Compile and publish a pipeline template.
• Create a pipeline run using the template and filter the runs.
• Manage (create, update, or delete) the pipeline template resources.

Vertex AI Pipelines now lets you configure task-level retries. You can set the number of times a task is retried before it fails. For more information about this option, see the Kubeflow Pipelines SDK Documentation.

Tabular Workflows is available in Preview. For documentation, refer to Tabular Workflows on Vertex AI.

End-to-End AutoML workflow is available in Public Preview. For documentation, refer to End-to-End AutoML.

Feature: Vertex AI Experiments is generally available (GA). Vertex AI Experiments helps users track and compare multiple experiment runs and analyze key model metrics.

Features supported by Experiments include:

• Vary and track parameters and metrics.
• Compare parameters, metrics, and artifacts between pipeline runs.
• Track steps and artifacts to capture the lineage of experiments.
• Compare vertex pipelines against Notebook experiments.

Vertex AI Forecasting is available in GA. The following features are available:

• Forecasting with ARIMA+
• Explainable AI for Forecasting
• Hierarchical Forecasting
• Model training with AutoML or Seq2Seq+
• Forecasting window configuration during model training
• Holiday effect modeling during model training

Support for IAM resource-level policies for Vertex AI featurestore and entityType resources is available in Preview.

M93 release

The M93 release of Vertex AI Workbench managed notebooks includes the following:

You can now configure the failure policy for a pipeline run.

The ability to configure Vertex AI private endpoints is now general available (GA). Vertex AI private endpoints provide a low-latency, secure connection to the Vertex AI online prediction service. You can configure Vertex AI private endpoints by using VPC Network Peering. For more information, see Use private endpoints for online prediction.

M91 release

The M91 release of Vertex AI Workbench managed notebooks includes the following:

• Log streaming to the consumer project via Logs Viewer is now supported.
• Added the net-tools package.
• Regular package refreshments and bug fixes.

You can now train your custom models using Cloud TPU Architecture (TPU VMs).

You can now use a pre-built container to perform custom training with PyTorch 1.11.

Vertex AI Model Registry is available in Preview. Vertex AI Model Registry is a searchable repository where you can manage the lifecycle of your ML models. From the Vertex AI Model Registry, you can better organize your models, train new versions, and deploy directly to endpoints.

Vertex AI Workbench is generally available (GA). Vertex AI Workbench is a single notebook surface for all your data science needs that lets you access BigQuery data and Cloud Storage from within JupyterLab, execute notebook code in Vertex AI custom training and Spark, use custom containers, manage costs with idle timeout, and secure your instances with VPC Service Controls and customer managed encryption keys (CMEK).

Features supported include:

• Google-managed instances and the latest GPU support
• Idle shutdown for managed notebooks instances
• Custom containers
• End-user and service account authentication
• Native plug-ins for BigQuery and Cloud Storage
• In-notebook Spark connect to Dataproc clusters
• Jobs support via the managed notebooks executor on Vertex AI custom training and Spark
• One-click deploy for NGC containers
• VPC Service Controls
• Customer managed encryption keys (CMEK)

The Vertex AI Workbench managed notebooks executor is generally available (GA). Use the executor to run notebook files on a schedule or as a one-time execution. You can use parameters in your execution to make specific changes to each run. For example, you might specify a different dataset to use, change the learning rate on your model, or change the version of the model. For more information, see Run notebook files with the executor.

Vertex AI Feature Store online store autoscaling is available in Preview. The online store nodes automatically scale to balance performance and cost with different traffic patterns. The offline store already scales automatically.

You can now mount Network File System (NFS) shares to access remote files when you run a custom training job. For more information, see Mount an NFS share for custom training.

This feature is in Preview.

Google Cloud Pipeline Components SDK v1.0 is now generally available.

You can now use a pre-built container to perform custom training with TensorFlow 2.8.

You can now use a pre-built container to perform custom training with PyTorch 1.10.

You can now use a pre-built container to perform custom training with TensorFlow 2.7.

Vertex AI TensorBoard is generally available (GA).

The autopackaging feature of the gcloud ai custom-jobs create command is generally available (GA). Autopackaging lets you use a single command to run code on your local computer as a custom training job in Vertex AI.

The gcloud ai customs-jobs local-run command is generally available (GA). You can use this command to containerize and run training code locally.

Vertex AI Pipelines is generally available (GA).

Using interactive shells to inspect custom training jobs is generally available (GA).

You can use these interactive shells with VPC Service Controls.

Vertex ML Metadata is generally available (GA).

Vertex AI Workbench is now available in Preview. Vertex AI Workbench is a notebook-based development environment for the entire data science workflow.

Vertex Feature Store is generally available (GA).

Vertex Matching Engine is generally available (GA).

Vertex AI Vizier is generally available (GA).

Vertex Explainable AI is generally available (GA).

You can use a pre-built container to serve predictions from TensorFlow 2.6 models.

Vertex Model Monitoring is generally available (GA).

When you perform custom training, you can access Cloud Storage buckets by reading and writing to the local filesystem. This feature, based on Cloud Storage Fuse, is available in Preview.

You can now use a pre-built container to perform custom training with TensorFlow 2.6 and PyTorch 1.9.

The following tools for creating embeddings to use with Vertex Matching Engine are available in Preview:

• the Two Tower built-in algorithm
• the Swivel pipeline template

You can use the Reduction Server algorithm (Preview) to increase throughput and reduce latency during distributed custom training.

The following features are generally available (GA):

• Access Transparency for Vertex AI
• Using a custom service account for custom training and prediction
• Using VPC Service Controls with Vertex AI
• Setting up VPC Network Peering with Vertex AI and using private IP for custom training (Using private IP for prediction and vector matching with Matching Engine remains in preview.)

Private endpoints for online prediction are now available in preview. After you set up VPC Network Peering with Vertex AI, you can create private endpoints for low-latency online prediction within your private network.

Additionally, the documentation for VPC Network Peering with custom training has moved. The general instructions for setting up VPC Network Peering with Vertex AI are available at the original link, https://cloud.google.com/vertex-ai/docs/general/vpc-peering. The documentation for custom training is now available here: Using private IP with custom training.

You can now use an interactive shell to inspect your custom training container while it runs. The interactive shell can be helpful for monitoring and debugging training.

This feature is available in preview.

You can now use the gcloud beta ai custom-jobs create command to build a Docker image based on local training code, push the image to Container Registry, and create a CustomJob resource.

You can now containerize and run your training code locally by using the new gcloud beta ai custom-jobs local-run command. This feature is available in preview.

You can now use NVIDIA A100 GPUs and several accelerator-optimized (A2) machine types for training. You must use A100 GPUs and A2 machine types together. Learn about their pricing.

Support for Compute Reservations. Notebooks API allows the use of Compute Reservations during instance creation.

You can now use a pre-built container to serve predictions from TensorFlow 2.5 models.

You can now use a pre-built container to serve predictions from XGBoost 1.4 models.

Vertex AI has added support for custom model training, custom model batch prediction, custom model online prediction, and a limited number of other services in the following regions:

• us-west1
• us-east1
• us-east4
• northamerica-northeast1
• europe-west2
• europe-west1
• asia-southeast1
• asia-northeast1
• australia-southeast1
• asia-northeast3

Vertex AI now supports forecasting with time series data for AutoML tabular models, in Preview. You can use forecasting to predict a series of numeric values that extend into the future.

Vertex Pipelines is now available in Preview. Vertex Pipelines helps you to automate, monitor, and govern your ML systems by orchestrating your ML workflow.

Vertex Model Monitoring is now available in Preview. Vertex Model Monitoring enables you to monitor model quality over time.

Vertex Feature Store is now available in Preview. Vertex Feature Store provides a centralized repository for organizing, storing, and serving ML features.

Vertex ML Metadata is now available in Preview. Vertex ML Metadata lets you record the metadata and artifacts produced by your ML system so you can analyze the performance of your ML system.

Vertex Matching Engine is now available in Preview. Vertex Matching Engine enables vector similarity search.

Vertex TensorBoard is now available in Preview. Vertex TensorBoard enables you to track, visualize, and compare ML experiments.

You can now use a pre-built container to serve predictions from TensorFlow 2.4 models.

You can now use a pre-built container to serve predictions from scikit-learn 0.24 models.

You can now use a pre-built container to serve predictions from XGBoost 1.3 models.

AI Platform Vizier is now available in preview. Vizier is a feature of AI Platform (Unified) that you can use to perform black-box optimization. You can use Vizier to tune hyperparameters or optimize any evaluable system.

The Python client library for AI Platform (Unified) is now called the AI Platform (Unified) SDK. With the release of version 0.7 (Preview), the AI Platform (Unified) SDK provides two levels of support. The high-level aiplatform library is designed to simplify common data science workflows by using wrapper classes and opinionated defaults. The lower-level aiplatform.gapic library remains available for those times when you need more flexibility or control. Learn more.

Cross Project Service Account is supported for user-managed notebooks.

You can now use a pre-built container to perform custom training with PyTorch 1.7.

AI Platform (Unified) now supports Access Transparency in beta. Google Cloud organizations with certain support packages can use this feature. Learn more about using Access Transparency with AI Platform (Unified).

In addition to Docker images hosted on Container Registry, you can now use Docker images hosted on Artifact Registry and Docker Hub for custom container training on AI Platform.

You can now use a pre-built container to perform custom training with TensorFlow 2.4.

You can now use a pre-built container to serve predictions from TensorFlow 2.3 models.

You can now use a pre-built container to serve predictions from XGBoost 1.2 models.

You can now use a pre-built container to perform custom training with PyTorch 1.6.

Notebooks Terraform Module supports Notebooks API v1

Notebooks API supports Shielded VM configuration.

Preview: Select AI Platform (Unified) resources can now be configured to use Customer-managed encryption keys (CMEK).

Currently you can only create resources with a CMEK key in the UI; this functionality is not currently available using the client libraries.

You can now use a pre-built container to perform custom training with TensorFlow 2.3.

AI Platform (Unified) now stores and processes your data only in the region you specify for most features. Learn more.

Preview release

AI Platform (Unified) is now available in Preview.

For more information, see the product documentation.

AI Platform Notebooks (now known as user-managed notebooks) API is now Generally Available. The API now includes an isUpgradable endpoint and adds manual and auto-upgrade functionality to notebooks instances created using the API.

Cloud Audit Logging for AI Platform Notebooks (now known as user-managed notebooks) is now Generally Available.

Granular IAM permissions for AI Platform Notebooks (now known as user-managed notebooks) is now Generally Available.

AI Platform Notebooks now supports E2 machine types.

The following new regions have been added:

• europe-west2 (London, UK)
• europe-west3 (Frankfurt, Germany)
• europe-west6 (Zürich, Switzerland)

AI Platform Notebooks (now known as user-managed notebooks) is now Generally Available. Some integrations with and specific features of AI Platform Notebooks are still in beta, such as Virtual Private Cloud Service Controls, Identity and Access Management (IAM) roles, and AI Platform Notebooks API.

VPC Service Controls now supports AI Platform Notebooks. Learn how to use a notebook instance within a service perimeter. This functionality is in beta.

AI Platform Notebooks now supports Access Transparency. Access Transparency provides you with logs of actions that Google staff have taken when accessing your data. To learn more about Access Transparency, see the Overview of Access Transparency.

You can now use customer-managed encryption keys (CMEK) to protect data on the boot disks of your AI Platform Notebooks (now known as user-managed notebooks) VM instances. CMEK in AI Platform Notebooks is generally available. For more information, see Using customer-managed encryption keys (CMEK).

AI Platform Notebooks now provides more ways for you to customize your network settings, encrypt your notebook content, and grant access to your notebook instance. These options are available when you create a notebook.

Now you can implement AI Platform Notebooks using custom containers. Use a Deep Learning Containers image or create a derivative container of your own, then create a new notebook instance using your custom container.

You can now create AI Platform Notebooks instances with R and core R packages installed. Learn how to install R dependencies, and read guides for using R with BigQuery in AI Platform Notebooks and using R and Python in the same notebook.

AI Platform Notebooks is now available in beta. AI Platform Notebooks enables you to create and manage virtual machine (VM) instances that are pre-packaged with JupyterLab and a suite of deep learning software.

Visit the AI Platform Notebooks overview and the guide to creating a new notebook instance to learn more.