Frequently Asked Questions
What is the focus of the AI Developer Tools track?
This track showcases the essential tools developers need to bring AI projects from prototype to production. Topics include orchestration frameworks, SDKs, tracking systems, and integration strategies for scalable AI workflows.
What frameworks and SDKs are covered?
Attendees will explore SDKs in Python and full-stack ML frameworks such as TensorFlow, PyTorch Lightning, and Hugging Face Transformers, with an emphasis on reproducibility, modularity, and API integration.
How do orchestration libraries support AI development?
Orchestration tools like LangChain and Guidance help developers manage prompts, memory, and logic flow across multi-agent or multi-model pipelines. This enables structured execution and fine-grained control of AI behavior.
What is MCP and why does it matter for developers?
The Multi-Agent Communication Protocol (MCP) provides a standard for secure and structured context sharing across LLM agents. This supports agent collaboration and transparency in complex GenAI applications.
How can experiment tracking tools enhance AI workflows?
Tools like MLflow, Weights & Biases, and Neptune help teams log experiments, manage model versions, and compare performance metrics—ensuring consistency across development cycles.