The session will highlight the most popular market trends and use-cases in building apps and agents for customers, and how partners can use the full Azure tech stack - app, data, and AI platforms, with the world's most popular developer tools, to offer their customers the solutions they need. The session will cover resources made available to partners to be successful.

In this technical deep-dive, Maria and Don unveil how Microsoft is shaping the future of agent-tool interactions through the Model Context Protocol (MCP). As AI agents evolve from simple task runners to autonomous collaborators, MCP emerges as the preferred communication mechanism—enabling secure, scalable, and OS-agnostic tool orchestration across platforms like Microsoft Foundry, GitHub, and VS Code.

Snowflake and Databricks both aim to provide data science toolkits for machine learning workflows, albeit with different approaches and resources. While developing ML models is technically possible using either platform, the Hitachi Solutions Empower team tested which solution will be easier, faster, and cheaper to work with in terms of both user experience and business outcomes for our customers. To do this, we designed and conducted a series of experiments with use cases from the TPCx-AI benchmark standard. We developed both single-node and multi-node versions of these experiments, which sometimes required us to set up separate compute infrastructure outside of the platform, in the case of Snowflake. We also built datasets of various sizes (1GB, 10GB, and 100GB), to assess how each platform/node setup handles scale.

Based on our findings, on the average, Databricks is faster, cheaper, and easier to use for developing machine learning models, and we use it exclusively for data science on the Empower platform. Snowflake’s reliance on third party resources for distributed training is a major drawback, and the need to use multiple compute environments to scale up training is complex and, in our view, an unnecessary complication to achieve best results.

Talk by: Michael Green and Don Scott

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