⚙️Engineering
⚙️
MLOps Fundamentals
Learn the practices and tools for deploying and maintaining ML systems in production
Prerequisites
🌱
Beginner
BeginnerMLOps basics
What to Learn
- •ML lifecycle: development to production
- •Version control for ML (Git, DVC)
- •Experiment tracking (MLflow, W&B)
- •Model packaging and serialization
- •CI/CD basics for ML
Resources
- 📚Made With ML MLOps course
- 📚MLflow documentation
- 📚Full Stack Deep Learning
🌿
Intermediate
IntermediateProduction MLOps practices
What to Learn
- •Feature stores (Feast, Tecton)
- •Model serving patterns
- •A/B testing for ML models
- •Data and model versioning
- •Monitoring and alerting
Resources
- 📚Feast documentation
- 📚Kubeflow pipelines
- 📚MLOps community resources
🌳
Advanced
AdvancedEnterprise MLOps
What to Learn
- •ML platform architecture
- •Automated retraining pipelines
- •Cost optimization for ML workloads
- •Governance and compliance
- •Multi-tenant ML infrastructure
Resources
- 📚Google ML Platform papers
- 📚Uber Michelangelo blog posts
- 📚Netflix ML infrastructure talks