Releasesby Glyph
Amazon SageMaker AI now supports instance preference lists for training and processing jobs
Today, Amazon SageMaker AI announces instance preference lists for training and processing jobs, making it easier and faster to find compute capacity for your workloads. Many AI training, fine-tuning, and data processing workloads run comparably well on any of several instance types or sizes.
Also shipped Sep 15, 2026AWS in September 2026 →
Predict dynamic table refresh behavior with EXPLAIN CHANGES (General availability)
You can now use EXPLAIN CHANGES to predict a dynamic table's refresh behavior before it happens, either for a manual refresh or as part of a DDL change, without applying the change.
Horizon Catalog Explorer UI (Preview)
With this release, the Horizon Catalog Explorer is the default experience for browsing Snowflake objects in Snowsight, replacing Database Explorer. The Horizon Catalog Explorer provides consistent navigation and object detail pages while preserving the tabs and actions that you already use.
Embedding Streamlit in Snowflake apps in external pages (General availability)
Embedding Streamlit in Snowflake apps in external pages is now generally available. You can render a container-runtime app inside a page you host, such as a customer portal or an internal dashboard, using a single tag.
Elastic Channels for Snowpipe Streaming (General availability)
Elastic Channels for Snowpipe Streaming are now generally available. Elastic Channels provide one implicit, server-scaled ingestion path per pipe, so concurrent producers can stream directly to Snowflake tables and supported Snowflake-managed Iceberg tables without managing channel names, lifecycle, ordering, or offset tokens.
Webhook-triggered deployments for Databricks Apps will soon be generally available
Starting in October 2026, webhook-triggered deployments for Databricks Apps will be generally available. Automatically deploy your app when changes are pushed to a connected GitHub or Azure DevOps repository, so your deployed app stays in sync with your repository without manual redeployment. See Deploy a Databricks app .
Sharing foreign Delta tables with OpenSharing is now generally available
Sharing and reading foreign Delta tables with OpenSharing is now generally available. Providers can share Delta-format tables federated from OneLake, a Hive metastore, or AWS Glue, without copying data into Databricks. See Add foreign Delta tables to a share .
More from Amazon SageMakerEvery Amazon SageMaker release →
Amazon SageMaker AI now supports serverless model customization for NVIDIA Nemotron 3.5 Lightning
Amazon SageMaker AI now supports serverless model customization for the NVIDIA Nemotron 3.5 Lightning model using supervised fine-tuning (SFT), Direct Preference Optimization (DPO), and reinforcement fine-tuning (RFT).
Qwen3.6-35B-A3B-NVFP4 and Wan2.1-T2V-1.3B-Diffusers models now available on Amazon SageMaker JumpStart
NVIDIA's Qwen3.6-35B-A3B-NVFP4 and Alibaba's Wan2.1-T2V-1.3B-Diffusers models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers.
Ministral-3-3B-Instruct-2512 and Ministral-3-8B-Instruct-2512 models now available on Amazon SageMaker JumpStart
Mistral AI's Ministral-3-3B-Instruct-2512 and Ministral-3-8B-Instruct-2512 models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers.
Gemma-4-31B-it-assistant and Gemma-4-31B-IT-NVFP4 models now available on Amazon SageMaker JumpStart
Google DeepMind's Gemma-4-31B-it-assistant and NVIDIA's Gemma-4-31B-IT-NVFP4 models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers.
granite-speech-4.1-2b, kanana-2-30b-a3b-instruct, and OpenFold3 models now available on Amazon SageMaker JumpStart
BM's granite-speech-4.1-2b, Kakao's kanana-2-30b-a3b-instruct, and the OpenFold Consortium's OpenFold3 models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers.
Amazon SageMaker HyperPod now supports model caching for faster inference autoscaling and reduced cold starts
Amazon SageMaker HyperPod now supports model caching, an inference optimization that pre-loads model weights and container images onto cluster nodes so pods start in seconds instead of minutes. When running LLM inference at scale for workloads like chat assistants, agentic pipelines, RAG, and document analysis, cold start is a real bottleneck.
Amazon SageMaker Unified Studio now supports ODBC connections for Microsoft Power BI and other analytics tools
Amazon SageMaker Unified Studio now supports ODBC connections, so data users can connect Microsoft Power BI and other ODBC-compatible tools directly to governed data in SageMaker Unified Studio projects using the Amazon Athena ODBC driver.
Amazon SageMaker Feature Store now supports individual feature updates to lower write latency
Amazon SageMaker Feature Store is a fully managed capability that makes it easy to compute, store, and retrieve features for training and deploying AI models. SageMaker Feature Store now supports feature-level writes, a new capability for updating individual features in a record.