MLOps for Beginners: From Jupyter Notebook to Production

Solve the "it works on my machine" problem once and for all. This technical blueprint bridges the gap between local data science experimentation and scalable, production-ready cloud deployment. Perfect for junior data scientists and DevOps engineers ready to professionalize their pipelines.

What’s Inside:

• The MLOps Lifecycle: A step-by-step framework covering ingestion, model serialization, and canary deployment patterns.
• MLflow vs. Kubeflow: An analytical breakdown comparing lightweight model registries with distributed Kubernetes orchestration.
• Docker & FastAPI: Hardened, security-focused containerization blueprints and high-performance REST server setups.
• Scaling Generative AI: Architecture guides for vLLM acceleration, RAG (Retrieval-Augmented Generation) networks, and distributed vector searching.
• Production Guardrails: How to implement automated statistical drift detection to monitor model performance before concept degradation occurs.