Releasesby Glyph
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.
Also shipped Sep 11, 2026AWS in September 2026 →
Automations in Snowflake CoWork (General availability)
Automations in Snowflake CoWork are now generally available. An automation turns a one-time report into a recurring one: Snowflake CoWork re-runs your question with fresh data on the schedule you choose and emails you the results, including a summary and key metrics, along with a link that opens the full report so you can ask follow-up questions.
Sharing foreign Iceberg tables with OpenSharing is now generally available
Sharing and reading foreign Iceberg tables with OpenSharing is now generally available. Providers can share Iceberg tables federated from foreign Iceberg catalogs using Lakehouse Federation, including to recipients using external Iceberg clients.
Sharing foreign schemas and tables with OpenSharing is now generally available
Sharing and reading foreign schemas and foreign tables with OpenSharing is now generally available. Providers can share foreign schemas and tables federated from external data sources using Lakehouse Federation, without copying data into Databricks.
Session restore for serverless jobs is in Public Preview
Restore the Python variables and Spark session from a serverless compute job run into a new interactive notebook, so you can debug a failure or explore results without rerunning the entire job. Databricks captures the snapshot automatically for eligible runs. See Session restore for serverless jobs .
Second generation Openflow deployments and runtimes now available on GCP
Second generation (gen 2) Openflow deployment and runtime SQL objects are now available on Google Cloud Platform (GCP), in addition to AWS and Azure. Previously, gen 2 deployment and runtime SQL objects were generally available only on AWS and Azure.
Add VS Code Agents to Copilot usage metrics
GitHub Copilot usage metrics reports now include generally available metrics for activity in the dedicated VS Code Agents window, helping you measure adoption and engagement across enterprises and organizations.
More from Amazon SageMakerEvery Amazon SageMaker release →
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.
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 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.
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.
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).