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Development Environment

Best practices for development workflows and collaboration

Continuous Integration
Automate testing and validation in development workflows

Learn CI pipelines, automated testing, code quality checks, build automation, and integration strategies for ML development workflows.

Code Best Practices
Development standards and practices for ML codebases

Explore coding standards, version control best practices, code review processes, documentation, and maintainable ML code patterns.

Team Collaboration
Workflow strategies for collaborative ML development

Understand collaboration workflows, branch strategies, experiment tracking, shared resources, and team coordination in ML projects.

Data Management
Handling data in development environments

Learn data versioning, synthetic data, data pipelines, feature stores, and data management practices for development.

Model Context Protocol
Standardized integration protocol for AI models and external tools

Explore MCP for connecting AI models to data sources, MLOps tools, and external services in a standardized way.