From the course: Deploying Scalable Machine Learning for Data Science

Scaling ML models

From the course: Deploying Scalable Machine Learning for Data Science

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Scaling ML models

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- [Instructor] Welcome to this course on scaling machine learning models. In this course, you'll learn about the difference between developing machine learning models and deploying them to a scalable production environment. We'll start by reviewing the model-building process, and discuss the requirements for ensuring that our applications are scalable. We will then examine three layers of a scalable machine learning stack. First is using services to expose machine learning models through APIs. Second, we'll look at containers for deploying models and related code to the production environment. And then we'll conclude with a look at orchestration tools for managing clusters of servers running our machine learning models. And we'll also discuss the need for monitoring in a production environment. So let's get started on scaling machine learning models.

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