Set up and optimize your local development workspace
Learn testing frameworks, unit testing for ML models, integration testing, mocking strategies, and test-driven development for ML projects.
Master Docker basics, Dockerfiles for ML, docker-compose, container orchestration, and best practices for local ML development environments.
Explore essential shell commands, automation scripts, environment management, file operations, and productivity tips for ML engineers.
Learn environment setup, virtual environments, dependency management, IDE configuration, and tools for productive local ML development.
Practice coding with LeetCode problems covering data structures, algorithms, and software engineering principles essential for MLOps development.