Best practices for development workflows and collaboration
Learn CI pipelines, automated testing, code quality checks, build automation, and integration strategies for ML development workflows.
Explore coding standards, version control best practices, code review processes, documentation, and maintainable ML code patterns.
Understand collaboration workflows, branch strategies, experiment tracking, shared resources, and team coordination in ML projects.
Learn data versioning, synthetic data, data pipelines, feature stores, and data management practices for development.
Explore MCP for connecting AI models to data sources, MLOps tools, and external services in a standardized way.