Inside Databricks AI Gateway: What Actually Changed
📰 Medium · Machine Learning
Learn about Databricks AI Gateway's new features, including LLM governance and MCP control, and how to apply them using observability SQL
Action Steps
- Run observability SQL queries to monitor LLM performance
- Configure MCP control to manage model access and permissions
- Apply LLM governance to ensure compliance and regulatory adherence
- Test AI Gateway's new features using sample datasets and workflows
- Compare the performance of different LLM models using AI Gateway's metrics and logging
Who Needs to Know This
Data engineers and machine learning engineers can benefit from understanding the new features and capabilities of Databricks AI Gateway to improve their workflow and model management
Key Insight
💡 Databricks AI Gateway provides a unified platform for managing and governing LLMs, enabling data engineers and ML engineers to streamline their workflows and improve model performance
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🚀 Explore Databricks AI Gateway's new features: LLM governance, MCP control, and observability SQL! 📊
Key Takeaways
Learn about Databricks AI Gateway's new features, including LLM governance and MCP control, and how to apply them using observability SQL
Full Article
A hands-on look at Unity AI Gateway — LLM governance, MCP control, and the observability SQL you can run this week Continue reading on Medium »
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