Setting Up Your MLOps Development Environment
Before pipelines, registries, and Kubernetes, every MLOps engineer needs a clean, reproducible local setup. Today you'll install Python 3.10+, master pip and virtual environments, and configure VS Code as a full ML workbench.
By Sandip Das · · 8 min read
💡
Hey — It's Sandip Das 👋
We need a reproducible MLOps development environment to build, test, and debug machine learning workflows consistently across local machines, CI/CD pipelines, and production platforms.
Mastering Python, virtual environments, and VS Code lays the foundation for managing scalable and reliable ML infrastructure with confidence.
We need a reproducible MLOps development environment to build, test, and debug machine learning workflows consistently across local machines, CI/CD pipelines, and production platforms.
Mastering Python, virtual environments, and VS Code lays the foundation for managing scalable and reliable ML infrastructure with confidence.
📚 Key Learnings
- Why Environment Setup Matters in MLOps
- Installing Python 3.10+ and pip
- Isolation: virtualenv vs conda
- VS Code: Your ML Workbench
- Verify Everything: Your First ML Notebook
- Troubleshooting Quick Reference