MLOps & CI/CD for AI
Never Stop Improving. Master experiment tracking (MLflow, Weights & Biases), automated evaluation pipelines, blue/green model deployments, data flywheel design, and the full AI SDLC.
Never Stop Improving. Master experiment tracking (MLflow, Weights & Biases), automated evaluation pipelines, blue/green model deployments, data flywheel design, and the full AI SDLC. This hands-on tutorial focuses on practical implementation of mlops & ci/cd for ai concepts.
MLOps & CI/CD for AI
Deploying an AI model is not the finish line — it's the starting gun. The best AI systems continuously improve using data from production. In this chapter, we build the operational infrastructure that turns production logs into better models.
[!NOTE] MLOps is where Machine Learning meets DevOps. It adds the concept of data drift, model decay, eval pipelines, and experiment tracking — concerns that don't exist in traditional software.
1. The AI Development Lifecycle 🔄
Why this matters: LLMs experience "model drift" not because the model changes, but because the world changes. A model trained in 2023 doesn't know about events in 2024. Your production data tells you what it doesn't know.
2. Experiment Tracking with MLflow 🔬
MLflow tracks every experiment you run — hyperparameters, metrics, model artifacts, and code versions — so you can reproduce any past result:
📚 Official Docs
3. Weights & Biases (W&B): Advanced Tracking 🎯
Weights & Biases goes beyond MLflow with real-time dashboards, sweep optimization, and LLM evaluation workflows:
📚 Official Docs
4. Automated Evaluation Pipelines: AI CI/CD 🤖
The gold standard: run evaluations automatically on every code change:
5. Blue/Green Model Deployments 🟢🔵
Deploy new models without downtime or risk:
6. The Data Flywheel 🌀
The most powerful AI companies don't just deploy models — they build systems that continuously collect high-quality labeled data from production:
Quiz
Quiz
Question 1 of 3What does MLflow track in an AI experiment?
Key Takeaways
✅ MLflow tracks every experiment with full reproducibility — never lose a good result again.
✅ W&B Sweeps automatically optimize RAG hyperparameters using Bayesian search.
✅ Automated eval pipelines on every PR prevent prompt regressions from reaching production.
✅ Blue/Green deployment enables zero-downtime model updates with instant rollback.
✅ The Data Flywheel is the sustainable competitive moat — production data → better models.
Official Resources
What's Next?
Congratulations — you've completed the full course. You now know how to build, secure, deploy, monitor, and continuously improve production AI systems.
Now go build something remarkable. 🚀