Explore advanced topics in machine learning engineering, including deep learning, generative AI, and the operationalization of large language models. Apply your knowledge to real-world use cases by designing and deploying ML microservices and ETML pipelines.
This course introduces learners to cutting-edge developments in deep learning and generative AI, with practical guidance on using frameworks like PyTorch and managing large language models in production. Learners will discover how to build, validate, and operationalize LLMs, as well as design and deploy end-to-end ML microservices and extract-transform-machine learning (ETML) solutions. The course emphasizes practical application, scalability, and integration of advanced ML techniques into business workflows. Combining up-to-date technical insights with real-world examples, this course empowers learners to tackle complex ML engineering challenges. Learners will develop the expertise to implement advanced ML solutions and drive innovation in their organizations. This course is part three 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.
