Releasesby Glyph
Amazon SageMaker Unified Studio adds light mode support for IDC-based domains
Amazon SageMaker Unified Studio now supports a light mode theme for Identity Center-based domains to improve accessibility.
Also shipped Oct 26, 2026AWS in October 2026 →
Conversation logging for Conversational analytics in BigQuery is generally available
BigQuery now supports logging data agent conversations directly to a BigQuery table within the agent's project.
Improved Lakeflow Performance Observability will soon be available by default for workspaces with the compliance security profile enabled
Databricks is enabling improved Lakeflow performance observability by default for workspaces with the compliance security profile.
Looker (Google Cloud core) data lineage is generally available
Data lineage for Looker (Google Cloud core) is now generally available, providing end-to-end visibility for LookML dashboards and Conversational Analytics agents.
BigQuery Python user-defined functions support volume mounts in preview
Python user-defined functions in BigQuery now support volume mounts, allowing users to access dependency files directly from Cloud Storage.
QuickBooks connector is available in Beta
The managed QuickBooks connector is now available in beta, enabling incremental ingestion of accounting data into Databricks.
Looker-managed Model Context Protocol (MCP) server is generally available
The Looker-managed MCP server is now generally available, allowing AI agents to interact with business data and LookML models without middleware.
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Amazon SageMaker AI announces training session APIs for serverless model customization
New training session APIs for SageMaker AI serverless model customization allow developers to define custom training loops for SFT, DPO, and reinforcement learning without managing GPU infrastructure.
Amazon SageMaker AI now supports serverless model customization for Muse Glimmer, Qwen 3.8 and Ministral 3 models
SageMaker AI now supports serverless model customization for Muse Glimmer, Qwen 3.8, and Ministral 3 models. Users can apply SFT, DPO, and RFT techniques using LoRA or full fine-tuning.
Amazon SageMaker AI now supports custom sequence lengths for serverless model customization jobs
Today, Amazon SageMaker AI announces the ability to configure the sequence length used during your serverless model customization job. Amazon SageMaker AI serverless model customization supports fine-tuning for many open-weight models, including models from popular families including gpt-oss, Gemma, Llama, Nemotron, and Qwen.
Nemotron 3.5 Super VL model now available on Amazon SageMaker JumpStart
NVIDIA's Nemotron 3.5 Super VL is now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. This vision-language model from the Nemotron 3.5 family accepts images, video, and text as input and generates text, enabling customers to deploy multimodal AI solutions on AWS infrastructure.
Beam model by Reflection is now available on Amazon SageMaker JumpStart and AWS Trainium
Beam, the first open weight model offered by US-based Reflection, is now available in Amazon SageMaker JumpStart, the model hub in Amazon SageMaker AI. According to Beam, they operate at the frontier of open weight models in its class, with strong performance across reasoning, coding, and agentic benchmarks.
Amazon SageMaker Studio simplifies getting started for new AI/ML builders with new sign-in options
Amazon SageMaker Studio now lets new customers sign up with their Google, GitHub, or Apple credentials and land inside a fully configured Studio environment - no traditional AWS account setup and no console navigation required.
Muse Spark Open model now available on Amazon SageMaker JumpStart
Meta's Muse Spark Open is now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. This coding-optimized open-weight model from Meta is purpose-built for agentic coding workflows, bringing multi-step reasoning and tool calling to enterprise software engineering on AWS infrastructure.
Amazon SageMaker Studio simplifies getting started for new AI/ML builders with social sign-in
Amazon SageMaker Studio now lets new customers sign up with their Google, GitHub, or Apple credentials and land inside a fully configured Studio environment - no traditional AWS account setup, no credit card, and no console navigation required.