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.

Choose Cost-Effective ML Algorithms Fast teaches you how to evaluate and compare machine learning algorithms based on their resource utilization—not just accuracy. In real ML pipelines, training time, memory footprint, and compute cost determine whether a model can run reliably at scale. In this short, practical course, you’ll examine how algorithm design affects efficiency, learn how to benchmark models fairly, and interpret logs to uncover cost patterns. You’ll complete a hands-on lab comparing XGBoost and Random Forest on a large dataset, charting training time and memory usage, and making a clear recommendation for the most cost-effective option. By the end of the course, you’ll know how to select algorithms that meet performance goals while staying efficient, predictable, and production-ready.

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.