Deploying a machine learning model into a fully realized production system requires painstaking work by engineering and operations teams to create and manage custom infrastructure. MLOps Engineering at Scale helps you bridge the gap from experimental models to production machine learning systems by relying on pre-built infrastructure and platform services from cloud vendors like AWS. Following a real-world use case for calculating taxi fares, you will learn how to engineer an MLOps pipeline for a PyTorch deep learning model using AWS server-less capabilities. Clear and detailed tutorials show you how to create reliable, flexible, and scalable machine learning systems without time-consuming operational tasks or the costly overheads of physical hardware.
about the technology
Your new machine learning model is ready to be put into production, but suddenly all your time is taken up by setting up your infrastructure. Engineering for MLOps (operationalizing machine learning with DevOps practices) offers a productivity-boosting and scalable alternative for creating and running production pipelines. It eliminates the time-consuming operations tasks from your machine learning lifecycle, letting out-of-the-box cloud services take over launching, running, and managing your ML systems. With the serverless capabilities of major cloud vendors handling your infrastructure, you’re free to focus on tuning and improving your models.
about the book
MLOps Engineering at Scale is a guide to bringing your experimental machine learning code to production using serverless capabilities from major cloud providers. You will start with best practices for your datasets, learning to bring VACUUM data-quality principles to your projects, and ensure that your datasets can be reproducibly sampled. Next, you’ll learn to implement machine learning models with PyTorch, discovering how to scale up your models in the cloud and how to use PyTorch Lightning for distributed ML training. Finally, you’ll tune and engineer your serverless machine learning pipeline for scalability, elasticity, and ease of monitoring with the built-in tools of your cloud platform. When you’re done, you’ll have the tools to easily bridge the gap between ML models and a fully functioning production system.
Extracting, transforming, and loading datasets
Querying datasets with SQL
Understanding automatic differentiation in PyTorch
Deploying trained models and pipelines as a service endpoint
Monitoring and managing your pipeline’s life cycle
Measuring performance improvements
about the reader
For data professionals with intermediate Python skills and basic familiarity with machine learning. No cloud experience required.
about the author
Carl Osipov has been working in the information technology industry since 2001, with a focus on projects in big data analytics and machine learning in multi-core, distributed systems, such as service-oriented architecture and cloud computing platforms. While at IBM, Carl helped IBM Software Group to shape its strategy around the use of Docker and other container-based technologies for serverless cloud computing using IBM Cloud and Amazon Web Services. At Google, Carl learned from the world’s foremost experts in machine learning and helped manage the company’s efforts to democratize artificial intelligence with Google Cloud and TensorFlow. Carl is an author of over 20 articles in professional, trade, and academic journals; an inventor with six patents at USPTO; and the holder of three corporate technology awards from IBM.
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