MLOps & Deployment
MLOps & Deployment on AI-ML Companion: Deploy, monitor, and maintain ML models in production. 14 interactive modules with live visualizations, quizzes, and hands-on Python coding.
Start free: What is MLOps? is fully open to everyone, no account required. The other 13 modules are part of AI-ML Companion Premium; every title and summary is listed below so you can see exactly what the track covers before deciding.
Modules in this track
- What is MLOps? (free) - The discipline of productionizing ML
- Docker Fundamentals (premium) - Containers, images, and Dockerfiles
- Docker for ML (premium) - Containerizing ML applications
- Building ML APIs (premium) - FastAPI and Flask for model serving
- CI/CD Fundamentals (premium) - GitHub Actions, automated testing
- CI/CD for ML (premium) - Automated training, testing, and deployment
- Experiment Tracking (premium) - MLflow, Weights & Biases, tracking runs
- Model Versioning (premium) - DVC, model registries, and artifact management
- Cloud Deployment (premium) - AWS SageMaker, GCP Vertex AI, Azure ML
- Cloud GPU & Hardware Selection (premium) - A100 vs H100, TPUs, cost optimization, and choosing the right accelerator
- Serverless ML (premium) - Lambda, Cloud Functions for inference
- Model Monitoring (premium) - Drift detection, performance tracking, alerts
- Kubernetes for ML (premium) - K8s basics for scaling ML workloads
- Project: Model Serving Platform (premium)