Python Refresher for ML Engineering Tasks
Master functions, classes, decorators, and file I/O. Graduate from notebooks to production-grade ML scripts. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 3)
100 Days of MLOps33
This roadmap takes you from zero MLOps knowledge to a production-ready ML engineer capable of building, deploying, monitoring, and automating end-to-end machine learning pipelines. You will master tools like MLflow, Docker, Kubernetes, Kubeflow, and cloud ML platforms while applying best practices in CI/CD, model governance, and observability. By Day 100, you will be able to design and operate scalable, reliable ML and LLM systems in real-world environments.
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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. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 2)
Understanding MLOps and Its Core Purpose
Machine learning models are only valuable in production. MLOps is the discipline that gets them there — and keeps them working. Let's build the foundation. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 1)