Extending AI Workflows with H2O ai APIs & Python SDKs | Part 18

What you'll learn

• H2O.ai provides Python SDKs, REST APIs, and MCP tools that enable full programmatic control over an enterprise AI platform.
• Developers can automate Driverless AI experiments, MLOps deployments, and h2oGPTe agents and integrate them into CI/CD pipelines.
• OpenAPI Swagger UIs allow developers to explore endpoints and generate client code in Python, JavaScript, or Go.
• The Model Context Protocol (MCP) server connects h2oGPTe agents directly to external systems like Salesforce, MongoDB, and GitHub.

Frequently asked questions

Topics

Description from the channel

How H2O.ai's Python SDKs, REST APIs, and MCP tools enable full programmatic control over the enterprise AI platform. No-code interfaces are valuable, but serious AI developers need deep programmatic flexibility. H2O.ai exposes Python SDKs, REST APIs, and hosted Jupyter Labs for scripting and automating every platform component—from triggering Driverless AI experiments to managing MLOps deployments within CI/CD pipelines. OpenAPI Swagger UIs allow developers to explore endpoints and generate client code in Python, JavaScript, or Go. The Model Context Protocol (MCP) server enables h2oGPTe agents to connect directly to external systems like Salesforce, MongoDB, and GitHub. Technical Capabilities & Resources ➤ Comprehensive SDKs & Libraries: Automate Driverless AI, MLOps, h2oGPTe, and Eval Studio via Python, JavaScript, and Go clients. 🔗 https://docs.h2o.ai/mlops/py-client/overview ➤ OpenAPI Specifications: Interactive Swagger UIs for exploring endpoints, testing calls, and generating client code. 🔗 https://h2ogpte.cloud-dev.h2o.dev/swagger-ui/ ➤ Agent Extensibility via MCP: Connect generative AI agents to external tools and proprietary data systems using the h2oGPTe MCP server. 🔗 https://pypi.org/project/h2ogpte-mcp-server/