Using Machine Learning for products, services, and critical business processes is quite different from using ML in an academic or research setting–especially for recent ML graduates and those moving from research to a commercial environment. Whether you currently work to create products and services that use ML, or would like to in the future, this practical book gives you a broad view of the entire field. Authors Robert Crowe, Hannes Hapke, Emily Caveness, and Di Zhu help you identify topics that you can dive into deeper, along with reference materials and tutorials that teach you the details. You’ll learn the state of the art of Machine Learning engineering, including a wide range of topics such as modeling, deployment, and MLOps. You’ll learn the basics and advanced aspects to understand the production ML lifecycle. If you’re working in ML AI or if you want to work in ML AI in any way other than pure research, this book is for you. It’s primarily focused on people who will have a job title of “ML engineer” or something similar, but in many cases, they’ll also be considered data scientists (the difference between the two job descriptions is often murky). On a more fundamental level, this book is for people who need to know about taking ML AI technologies and using them to create new products and services. Putting models and applications into production might be the main focus of your job, or it might be something that you do occasionally, or it might even be something done by a team you collaborate with.

Machine Learning Production Systems: Engineering Machine Learning Models and Pipelines PDF

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