Databricks releases, every cloud
- You can now use custom base environments for Python, Python Wheels, and notebook tasks in serverless jobs
Defined with YAML files, they support Python, Python wheel, and notebook tasks.
- New resource types for Databricks Apps
Adds MLflow experiments, vector search indexes, UDFs, and Unity Catalog connections as resources.
- Configure compute size for Databricks Apps (Public Preview)
Controls CPU, memory, and cost based on workload requirements.
- List MCP servers in Databricks Marketplace (Public Preview)
Lets users install MCP servers to connect AI agents to external data sources.
- Serverless notebook tasks can now use jobs environment
Replaces inherited serverless environment with configurable job environment.
- Serverless compute has been updated to version 17.3
Updated from version 17.2, requires no configuration changes.
- Run the change, do not just read it. Hands-on labs in your own Databricks workspace, graded when you submit. Browse labs
- The billable usage table now records the performance mode of serverless jobs and pipelines
Records performance mode in product_features.performance_target column with values PERFORMANCE_OPTIMIZED, STANDARD, or null.
- Admins can now manage a workspace's serverless base environments (Public Preview)
Defines custom environment specs for serverless notebooks, allows setting a default for new notebooks.
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