Master the skills needed to package, deploy, and scale machine learning solutions for real-world applications. Explore modern deployment patterns, containerization, and scalable infrastructure to ensure your ML models are production-ready.
This course delves into the practical aspects of preparing machine learning code for production, including writing maintainable Python, adhering to coding standards, and securing your solutions. Learners will examine deployment patterns and tools, such as microservices, containers, and orchestration platforms like AWS, ECS, Airflow, and Kubeflow. The course also covers strategies for scaling ML systems using Spark, Kubernetes, and Ray, enabling learners to design and manage robust, scalable ML infrastructures. With a focus on industry-relevant practices and step-by-step explanations, this course guides learners through the technical and architectural decisions required to deploy and scale ML solutions effectively. Learners will gain confidence in building resilient and secure ML systems for diverse business needs. This course is part two of a three-course Specialization designed to build a complete and cohesive understanding of the subject. While it offers valuable skills on its own, you'll gain the most benefit by progressing through all three courses as a structured learning journey. This course is based on Machine Learning Engineering with Python, by Andrew P. McMahon. Packt is one of the world's most prolific publishers of cutting-edge technical content. For over two decades we've made it our mission to curate and publish the knowledge of only the very best technical experts. We focus on real-world courses that help our customers get the job done, with coverage that extends across a wide range of established and cutting-edge technical topics. If you're an individual or an organisation that embraces learning by doing, Packt is the perfect fit for you.
