Lilly Tech Systems
Learn MLOps
Master the practices for deploying and maintaining machine learning models in production — from data pipelines and model training to deployment, monitoring, and CI/CD for ML.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is MLOps? Why it matters, maturity levels, key roles, and the MLOps tools landscape.
2. ML Lifecycle
The end-to-end ML project lifecycle from problem definition through monitoring and retraining.
3. Data Pipeline
Data ingestion, validation, feature stores, versioning, and orchestration with Airflow.
4. Model Training
Experiment tracking, hyperparameter tuning, distributed training, and model registries.
5. Model Deployment
Deployment patterns, serving frameworks, containerization, Kubernetes, and A/B testing.
6. Monitoring
Data drift, concept drift, performance monitoring, alert systems, and observability tools.
7. CI/CD for ML
CI/CD pipelines, testing ML code, model validation, and pipeline orchestration platforms.
8. Best Practices
Team structure, documentation, cost optimization, security, governance, and common pitfalls.
What You'll Learn
By the end of this course, you'll be able to:
Design ML Pipelines
Build robust, automated data and model pipelines that scale from experiment to production.
Deploy ML Models
Choose and implement deployment patterns: batch, real-time, edge, with proper A/B testing.
Monitor in Production
Detect data drift, concept drift, and performance degradation before they impact users.
Automate ML Workflows
Set up CI/CD for ML with automated testing, validation, and deployment pipelines.