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1. Initializing

In MLOps, the initialization phase is your foundation for building scalable, reproducible, and maintainable machine learning systems. A well-configured development environment prevents common but costly issues like dependency conflicts, version mismatches, and "it works on my machine" problems.

By standardizing your setup from day one, you establish best practices that ensure smooth collaboration, automated workflows, and reliable deployments. This chapter guides you through creating a professional-grade environment tailored for the demands of MLOps projects.

  • 1.0. System: This section outlines the essential prerequisites and system-level configurations needed to run key development tools effectively.

  • 1.1. Python: Learn how to manage multiple Python versions and create isolated project environments—a fundamental practice for avoiding dependency conflicts and ensuring reproducibility.

  • 1.2. uv: Discover uv, an all-in-one tool written in Rust that dramatically speeds up Python version management, virtual environments, and dependency resolution, boosting your productivity.

  • 1.3. uv (project): This section focuses on using uv to define, install, and manage your project's metadata and dependencies, simplifying the packaging process.

  • 1.4. git: Master git, the industry-standard version control system. This section covers the core commands for initializing repositories and managing code changes, a vital skill for any developer.

  • 1.5. GitHub: How do you use GitHub for collaborative MLOps projects?** Learn to leverage GitHub for hosting repositories, managing collaborative workflows, and integrating with CI/CD pipelines. GitHub is the central hub for modern software and MLOps development.

  • 1.6. VS Code: How can you optimize VS Code for MLOps?** Transform VS Code from a simple text editor into a powerful Integrated Development Environment (IDE) tailored for Python and MLOps, complete with debugging, linting, and extension recommendations.