Master Production ML Systems. Deploy, monitor, and scale ML models using industry-standard tools and practices from Netflix, Uber, and Airbnb.
Bridge the gap between ML experimentation and production systems
Master Docker, Kubernetes, cloud platforms (AWS, GCP, Azure), and tools like MLflow, FastAPI, BentoML to deploy and scale production ML systems
Detect data drift, track performance, set up alerts, and create end-to-end automated workflows with Airflow, Kubeflow for CI/CD
Build portfolio projects showcasing production ML skills. Handle millions of predictions with optimization and land $120k-$200k+ MLOps roles
The Critical Gap Between Notebooks and Production
90% of ML models never make it to production. Why? Because building a model is only 10% of the work. The real challenge is:
MLOps is the solution. This course teaches you the engineering skills to take any ML model from prototype to production at scale.
💻 System Requirements: Computer with 8GB+ RAM, modern OS (Windows/Mac/Linux), 20GB free disk space for Docker and tools
MLflow, Weights & Biases, DVC
FastAPI, BentoML, TorchServe, TensorFlow Serving
Docker, Docker Compose, Container Registries
Apache Airflow, Kubeflow, Prefect, MLflow Pipelines
AWS (SageMaker, Lambda, ECR), GCP, Azure ML
Prometheus, Grafana, Evidently AI, WhyLabs
Kubernetes, Terraform, Helm
GitHub Actions, GitLab CI, Jenkins
Understand the MLOps lifecycle and core concepts
Understand the ML vs MLOps lifecycle, production challenges (data drift, model decay), maturity levels, and case studies from Netflix, Uber, Airbnb
Start Learning →Master experiment tracking with MLflow and Weights & Biases, model versioning, reproducibility, and collaboration best practices
Start Learning →Learn model formats (Pickle, Joblib, ONNX, SavedModel, TorchScript), creating artifacts, and dependency management
Start Learning →Deploy ML models to production with APIs and containers
Build production-ready APIs with FastAPI. Request validation, async predictions, health checks, authentication, rate limiting
Start Learning →Master Docker for ML. Write Dockerfiles, multi-stage builds, Docker Compose, GPU support, and optimize image sizes
Start Learning →Deploy to AWS SageMaker, Google Cloud AI Platform, Azure ML. Serverless ML with Lambda. Cost optimization strategies
Start Learning →BentoML, TorchServe, TensorFlow Serving. Batch vs real-time inference, load balancing, A/B testing, blue-green deployments
Start Learning →Build automated pipelines and scalable infrastructure
Apache Airflow, Kubeflow Pipelines, MLflow Pipelines. Build end-to-end automated workflows and CI/CD for ML models
Start Learning →K8s fundamentals, deploying ML models, Horizontal Pod Autoscaling, GPU scheduling, Helm charts for ML applications
Start Learning →Terraform for ML infrastructure, CloudFormation, automated environment provisioning, managing secrets with Vault
Start Learning →Keep models healthy and performant in production
Track model performance metrics, detect data drift with Evidently AI and WhyLabs, set up alerting with Prometheus and Grafana, implement structured logging for production observability
Start Learning →Automated retraining pipelines, trigger conditions, online vs batch learning, shadow mode testing, rollback strategies
Start Learning →Implement feature stores with Feast and Tecton, establish model governance and compliance frameworks, optimize costs and resource usage, secure ML systems, create comprehensive documentation with model cards
Start Learning →Build production ML systems with real-world tools
Build complete pipeline from data ingestion to deployment. Use MLflow tracking, Docker packaging, FastAPI deployment with monitoring dashboard
Start Project →Deploy model on Kubernetes with autoscaling, Redis caching, load balancer, and Grafana monitoring for latency/throughput
Start Project →Deploy model with Evidently AI drift detection, set up alerting system, implement automated retraining trigger with Airflow
Start Project →Build BentoML-based platform with A/B testing, model versioning, rollback mechanisms, and per-model cost tracking
Start Project →MLOps is one of the fastest-growing tech roles
Entry Level: $100k - $130k
Mid Level (3-5 yrs): $140k - $180k
Senior Level (5+ yrs): $180k - $250k+
Plus equity, bonuses, and benefits at top companies
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