MLOps & Production ML
Shipping is the hard part. Experiment tracking, CI/CD for ML, Kubernetes, monitoring, IaC - build ML systems that hold up under real production load.
15 Modules. Dev to Production.
From experiment tracking to cost management - every layer of production ML, explained with depth.
MLOps Foundations
What MLOps actually is, why it matters, the maturity model, and the full ML lifecycle from experimentation to production.
What you'll master
- What Is MLOps & Why It Matters
- The ML Lifecycle End-to-End
- MLOps Maturity Model (Level 0→3)
- Key Principles: Automation, Reproducibility, Monitoring
- Tooling Landscape Overview
5 lessons
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Experiment Tracking
MLflow, Weights & Biases, and Neptune - log every run, compare experiments, and never lose a result again.
What you'll master
- Why Experiment Tracking Exists
- MLflow: Tracking, Projects, Models
- Weights & Biases in Depth
- Hyperparameter Optimization
- Comparing Runs & Analysis
- Distributed Training Tracking
6 lessons
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Data Versioning
DVC, Delta Lake, and LakeFS - version your datasets, reproduce any training run, and prevent data drift silently corrupting models.
What you'll master
- Why Data Versioning Matters
- DVC: Git for Data
- Delta Lake & Time Travel
- LakeFS for Data Lake Versioning
- Dataset Lineage & Provenance
5 lessons
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Model Registry
Centralized model lifecycle management - staging, production promotion, lineage tracking, and governance across teams.
What you'll master
- Model Registry Architecture
- MLflow Model Registry
- Model Versioning & Lineage
- Staging → Production Workflows
- Model Cards & Documentation
- Governance & Approval Gates
6 lessons
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CI/CD for ML
Automated testing, validation pipelines, and deployment workflows - ship ML models with the same rigor as software.
What you'll master
- CI for ML: Testing Code & Data
- Automated Model Validation
- CD Strategies (blue-green, canary, shadow)
- GitHub Actions & GitLab CI for ML
- Model Quality Gates
- Rollback & Incident Response
- End-to-End ML Pipeline Automation
7 lessons
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Containerization
Docker, multi-stage builds, container registries, and reproducible ML environments - end the "works on my machine" era.
What you'll master
- Docker for ML Workloads
- Multi-Stage Build Optimization
- GPU-Enabled Containers
- Container Registries (ECR, GCR, ACR)
- Docker Compose for ML Development
- Security & Image Hardening
6 lessons
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Pipeline Orchestration
Airflow, Prefect, Kubeflow Pipelines, Metaflow, and ZenML - orchestrate complex ML workflows that reliably run at scale.
What you'll master
- Orchestration Concepts & DAGs
- Apache Airflow for ML
- Prefect 2.x / 3.x
- Kubeflow Pipelines v2
- Metaflow (Netflix)
- ZenML & Stack Abstraction
- Choosing the Right Orchestrator
7 lessons
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Kubernetes for ML
Pods to production ML - scheduling, autoscaling, GPU node pools, KServe inference, and the Kubernetes patterns every MLOps engineer must own.
What you'll master
- Kubernetes Fundamentals for ML
- GPU Node Pools & Scheduling
- KServe for Model Serving
- KEDA for Inference Autoscaling
- Helm Charts for ML Platforms
- Operators: Argo, Seldon, Ray
- Multi-Tenant ML Clusters
7 lessons
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Monitoring & Observability
Data drift, model degradation, prediction monitoring, and alerting - catch problems before users do.
What you'll master
- Data Drift Detection
- Model Performance Monitoring
- Prediction & Outcome Tracking
- Evidently AI & Whylogs
- Prometheus & Grafana for ML
- Alerting & On-Call for ML
- Root Cause Analysis Workflows
7 lessons
Start for Free →
Cloud ML Platforms
AWS SageMaker, Google Vertex AI, Azure ML, and Databricks - the managed platforms that run production ML at hyperscaler scale.
What you'll master
- AWS SageMaker End-to-End
- Google Vertex AI Pipelines
- Azure ML & MLflow Integration
- Databricks for MLOps
- Cloud Cost Optimization
5 lessons
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A/B Testing & Experimentation
Statistical rigor for ML experiments - controlled experiments, multi-armed bandits, interleaving, and counterfactual evaluation.
What you'll master
- Statistical Foundations
- Online Controlled Experiments
- Shadow Mode Testing
- Multi-Armed Bandits
- Interleaving Experiments
- Counterfactual Evaluation
- Experimentation Platforms
7 lessons
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LLMOps Pipelines
Fine-tuning ops, prompt management, LLM evaluation pipelines, RAG pipeline ops, and token cost monitoring at production scale.
What you'll master
- Fine-Tuning Ops (LoRA, adapters)
- Prompt Management & Versioning
- LLM Evaluation Pipelines
- RAG Pipeline Operations
- Token Cost Monitoring
5 lessons
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Infrastructure as Code
Terraform, Pulumi, GitOps, and environment parity - reproducible ML infrastructure from dev laptop to GPU cluster.
What you'll master
- IaC for ML Teams
- Terraform Fundamentals
- Terraform for ML Infrastructure
- Pulumi (Python-native IaC)
- GitOps with Flux & ArgoCD
- Environment Parity (dev→prod)
- IaC Patterns for ML Platforms
7 lessons
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Feature Engineering at Scale
Batch and streaming feature pipelines, feature stores, training-serving skew prevention, and automated feature selection.
What you'll master
- Feature Engineering Fundamentals
- Numerical & Categorical Features
- Text & Embedding Features
- Time-Series Features
- Feature Stores (Feast, Tecton)
- Feature Validation & Testing
- Feature Selection & Importance
- Automated Feature Engineering
8 lessons
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Cost Management for ML
ML unit economics, cloud FinOps, training cost optimization, inference cost reduction, and build vs buy analysis.
What you'll master
- ML Unit Economics
- Training Cost Optimization
- Inference Cost Optimization
- Cloud FinOps for ML
- Build vs Buy Analysis
- Cost Attribution & Chargeback
6 lessons
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Ready to ship ML systems that don't break in production?
From experiment tracking to Kubernetes - the complete MLOps curriculum.
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