Machine Learning Operations (MLOps)

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Overview

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This three-course specialization is built for ML practitioners and software engineers who want to stop experimenting and start shipping. You will master the engineering practices required to take trained models from notebooks to production — focusing on DevOps automation, cloud deployment, and containerized serving rather than model theory. Starting with DevOps foundations, you will build automated ML training pipelines with GitHub Actions, serve models through FastAPI, and implement CI/CD workflows from code to deployment using Docker. As you progress, you will gain a comprehensive understanding of cloud ML platforms across AWS, Azure, and GCP — learning when to use SageMaker, Vertex AI, or Azure ML Studio, and how to evaluate build-vs-buy decisions for managed ML services. The final course takes you deep into production model serving — building Dockerized ML services from scratch, designing multi-model serving APIs with versioning and A/B testing, optimizing prediction latency, and implementing batch and real-time inference patterns. By the end, you will have the engineering toolkit to reliably ship, serve, and scale ML models across any deployment environment.

Syllabus

Course 1: DevOps for Machine Learning: CI/CD, APIs & Deployment
Course 2: Cloud Platforms for ML: AWS, Azure & GCP Deployment
Course 3: Model Serving Systems: Containers, APIs & Scalability

Courses

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"Cloud ML Platforms: AWS, Azure, and GCP for ML Engineers is designed for aspiring cloud ML engineers, data scientists, and developers looking to master enterprise ML deployment across the top three cloud providers. You'll learn to deploy, scale, and integrate machine learning models using SageMaker, Azure ML Studio, Vertex AI, BigQuery ML, and serverless functions — while building skills to evaluate and choose the right cloud platform for any business need. The first module dives into the AWS ML ecosystem, where you'll explore SageMaker, Lambda, S3, and Glue to build end-to-end data pipelines and deploy models as scalable endpoints. The second module introduces Azure ML Studio, Azure Functions, and Cognitive Services, enabling low-code workflows, serverless inference, and integration with pre-built NLP and Vision APIs. The third module covers Google Cloud's ML stack — Vertex AI, BigQuery ML, and Cloud Functions — giving you hands-on exposure to unified workflows, SQL-based modeling, and event-driven deployment. The final module equips you with evaluation frameworks to compare AWS, Azure, and GCP on cost, scalability, and integration, helping you make confident build-vs-buy and platform selection decisions. By the end of this course, you will: - Deploy ML models across AWS SageMaker, Azure ML, and Vertex AI using managed services - Build serverless inference workflows with Lambda, Azure Functions, and Cloud Functions - Evaluate cost, scalability, and vendor lock-in trade-offs across major cloud ML platforms - Recommend the right cloud ML platform aligned with enterprise business goals"
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"DevOps Foundations for ML is designed for aspiring MLOps engineers, data scientists, and developers who want to bring DevOps discipline into machine learning workflows. You'll learn to automate, test, containerize, and deploy ML models using Git, GitHub Actions, Docker, and FastAPI — building production-ready pipelines end to end. The first module builds your foundation in version control and automation. You'll configure Git repos, adopt branching strategies, and use GitHub Actions to automate testing and linting of ML code. The second module focuses on ML pipeline automation. You'll design multi-stage CI/CD workflows that handle data preprocessing, training, evaluation, and automated retraining with secure secret management. The third module teaches you to serve ML models as real-time REST APIs using FastAPI, covering input validation, latency optimization, testing, and OpenAPI documentation. The final module covers packaging and deployment. You'll containerize ML services with Docker, optimize image size, and automate deployments to cloud runners with monitoring. By the end of this course, you will: - Build CI/CD pipelines with GitHub Actions for automated ML testing and retraining - Develop and test ML REST APIs using FastAPI with validation and OpenAPI docs - Containerize ML services with Docker and deploy them to production - Apply version control and automated testing best practices for reproducible ML"
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"Docker and Model Serving: Deploy ML APIs with FastAPI and ONNX is designed for ML engineers, MLOps practitioners, and backend developers who want to take models from notebooks to production. You'll learn to build Docker containers for ML workloads, design scalable REST APIs with FastAPI, serialize models with ONNX and SavedModel, and deploy with zero-downtime strategies like blue-green and canary releases. The first module covers Docker fundamentals, image optimization, multi-stage builds, secrets management, and Docker Compose for multi-container ML apps. The second module focuses on REST API design with FastAPI, model versioning, input validation with Pydantic, structured logging, and production-grade error handling. The third module teaches scaling strategies — horizontal scaling, async queues, load balancing, batch vs. real-time inference, and latency optimization for high-throughput serving. The final module covers model serialization formats (ONNX, pickle, SavedModel), blue-green and canary deployments, automated rollback, and disaster recovery. By the end of this course, you will: - Build and optimize Docker images for ML models using multi-stage builds and Compose - Design scalable FastAPI endpoints with versioning, validation, and observability - Scale ML inference with async queues, load balancing, and latency optimization - Deploy models with ONNX serialization and zero-downtime blue-green rollbacks"