ML Production Systems

Containerization is more than a deployment tool—it’s the backbone of reliable, scalable machine learning systems. In this intermediate-level course, you’ll learn how to package, deploy, and manage ML models using Docker and Kubernetes. You’ll start by exploring why containerization matters—how it ensures reproducibility and stability across environments. Then, you’ll move into orchestration, learning how Kubernetes automates deployment, scaling, and monitoring for real-world applications. Through concise videos, guided readings, and a hands-on project, you’ll write a Dockerfile, publish your image to an internal registry, and deploy it to a cluster using a Kubernetes configuration file. You’ll also practice testing and reflecting on your deployment process to strengthen your operational mindset. By the end, you’ll be able to build, deploy, and manage containerized ML applications confidently—skills essential for engineers, data scientists, and anyone bringing AI models into production.

Did you know that over 70% of machine learning failures in production stem from fragile, untested code rather than faulty models? Test-driven development is the key to writing ML pipelines that are reliable, reusable, and production-ready. This Short Course was created to help professionals in this field develop robust and maintainable ML code that meets production standards and enables effective team collaboration. By completing this course, you will be able to write modular ML components, build test-driven data loaders and training loops, and ensure your codebase is resilient to change and easy for teams to maintain—skills that strengthen both software quality and ML workflow reliability. By the end of this 3-hour long course, you will be able to: Apply modular and test-driven development principles to code data loaders and training loops. This course is unique because it merges software engineering best practices with practical ML development, giving you hands-on experience in creating clean, testable, and scalable ML code that supports long-term production success. To be successful in this project, you should have: Python programming experience Basic ML concepts Familiarity with TensorFlow Unit testing fundamentals

Are you deploying ML models that need to respond in milliseconds, not seconds? In production environments, even the most accurate model becomes worthless if it can't meet real-time performance demands. This Short Course was created to help ML and AI professionals accomplish systematic optimization of inference code and establish robust development workflows for production-ready ML systems. By completing this course, you'll be able to diagnose performance bottlenecks in your inference pipelines, apply advanced optimization techniques like quantization and pruning, and implement GitFlow or Trunk-Based Development strategies with automated CI/CD pipelines that you can deploy immediately in your workplace. By the end of this course, you will be able to: - Analyze inference code to optimize for real-time performance - Evaluate Git branching strategies and CI/CD pipelines for codebase management This course is unique because it bridges the gap between ML model development and production engineering, combining performance optimization techniques with software engineering best practices specifically tailored for ML workflows. To be successful in this project, you should have experience with Python, PyTorch or TensorFlow, TensorRT, Git version control, and basic understanding of ML model deployment.

Transform your Kubernetes infrastructure from reactive to intelligent with advanced resource optimization strategies that power today's most demanding ML and AI workloads. This Short Course was created to help Machine Learning and AI professionals accomplish systematic resource optimization in production Kubernetes environments. By completing this course, you'll master the critical skills to analyze resource utilization patterns, configure Horizontal Pod Autoscalers with precision, and implement cost-effective scaling strategies that maintain optimal performance under varying workloads. By the end of this course, you will be able to: • Analyze resource utilization metrics across pods and nodes to identify scaling opportunities • Configure and tune Horizontal Pod Autoscalers based on CPU, memory, and custom metrics • Implement resource requests and limits that prevent contention while optimizing costs This course is unique because it combines real-world production scenarios with hands-on dashboard analysis and HPA tuning exercises that mirror the challenges faced by ML infrastructure teams managing GPU-intensive workloads. To be successful in this project, you should have a background in basic Kubernetes concepts, container orchestration, and system monitoring.

Take your machine learning skills beyond the notebook and into production. In this short, practical course, you’ll learn how to turn trained models into reliable RESTful inference services, automate deployment pipelines, and monitor real-time performance like a professional MLOps engineer. You’ll build a /predict API using FastAPI, integrate it with GitHub Actions for CI/CD, and then simulate traffic with Locust to evaluate latency and optimize for a 100 ms SLA target. Whether you’re an aspiring MLOps engineer or a data scientist ready to bridge into deployment, this course gives you the hands-on confidence to deliver production-grade ML services that scale. You’ll strengthen the technical and analytical skills that modern AI teams need — automation, performance optimization, and service reliability — to stay competitive in the evolving ML operations landscape. By the end, you’ll not only deploy your own model confidently but also gain the credibility to manage real-world ML systems end-to-end.

This course teaches you how to build a fully automated machine learning pipeline using scikit-learn. You will learn to scale numeric features, encode categorical variables, train a logistic model, and optimize it using GridSearchCV. The course then guides you in packaging the workflow as a reusable module that fits real-world ML engineering and MLOps practices. Through concise videos, structured readings, two 15-minute Coach interactions, a combined 25-minute hands-on activity, and a 45-minute ungraded lab, you will practice constructing and refining an end-to-end pipeline. By the end, you will have a polished, automated workflow you can reuse, adapt, and integrate into your ML projects or production systems.