Articles about Machine Learning, LLMs, RAG, MLOps and AI deployment.
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Short answer: MLOps is the practice of getting machine learning models into production and keeping them useful there — versioning data and models, making training reproducible, automating deployment, and monitoring for the drift that quietly degrades every model. It is DevOps plus two things software does not have: data as a dependency, and silent failure.
Short answer: two to four weeks for the basics, three to six months to build real things, and four to twelve months to be job-ready — driven almost entirely by hours per week. Five hours weekly puts job readiness around a year; fifteen to twenty pulls it to four or six months.
Short answer: use RAG when the answer depends on facts that change, or when you need citations. Fine-tune when the problem is behaviour — a fixed format, a specific tone, or a small model that must match a large one on one narrow task. Around 80 percent of applications start with RAG, and most mature systems end up with both.