Most LLM courses end where the real work begins. Getting a good response is easy, deciding whether a change deserves to ship is not. This bootcamp builds one assistant called OpsMate and takes it through the full lifecycle, from model internals through RAG, evaluation gates, fine-tuning, deployment, and observability.

This Course Offers

• Model fundamentals covering tokens, prefill, decode, attention, and the KV cache, and how they drive latency, memory, and cost
• Serving models behind an OpenAI compatible API with practical comparison of Ollama, llama.cpp, and vLLM
• RAG pipeline construction with diagnosis of chunking, retrieval, grounding, and prompt injection failures
• Evaluation practice building a golden set and baseline that turns better into a ship or reject decision, plus a laptop scale LoRA fine tune
• Packaging, signing, and versioning model artifacts with an OCI supply chain workflow, then canary deployment on Kubernetes with promote or rollback on quality evidence

Why We Love This Course

1. It runs on an average 8 GB laptop with no GPU, no cloud account, and no paid API, which removes the usual barrier to production LLM practice.
2. The labs are built around evidence and failure. Watching a fine tuned model get blocked by its own quality gate teaches more than a clean success.
3. Observability is covered properly, including TTFT, ITL, queue depth, and token cost, with a warning against trusting misleading averages.
4. It ends with a gateway covering virtual keys, budgets, caching, fallbacks, guardrails, and CI eval gates, plus a bounded tool calling agent over MCP.

The question that actually matters in this work is whether a model change is ready to promote, and what evidence supports that. Do you want to keep shipping on intuition, or build a release process you can defend? Enrollment is open, and no machine learning background is needed.

• This course is perfect for DevOps, SRE, and platform engineers moving into LLM and GenAI operations.
• It suits backend and application engineers who need to take an LLM feature past a local demo.
• MLOps engineers extending from predictive models, and technical leads needing a defensible model release framework, will find the full lifecycle covered, though it is not for those seeking prompt writing tips or a Kubernetes introduction.

• A laptop with at least 8 GB RAM and around 40 GB free disk is needed for local models and containers.
• Comfort with the command line, Git, containers, and Docker Compose is expected.
• Basic Kubernetes knowledge is helpful but not required, and no machine learning background, GPU, cloud account, or paid API key is needed.

Interested in exploring more lessons? Check out our full course library to continue building your skills and advancing your learning journey.