Design Principles for Machine Learning Framework
This course will teach you the important design principles for machine learning. You will learn what data pipelines are, where to use them, and why.
What you'll learn
Design patterns capture best practices and solutions to common problems. In this course, Design Principles for Machine Learning Framework, you’ll learn to implement scalable data pipelines for machine learning systems. First, you’ll explore guiding principles for machine learning operations. Next, you’ll discover why you should use data pipelines to process incoming data in real-time. Finally, you'll learn how to evaluate the performance of a machine learning system. When you’re finished with this course, you’ll have the skills and knowledge of machine learning operations needed to orchestrate a scalable and powerful system.
Table of contents
- Overview 1m
- Introducing Machine Learning Pipelines 2m
- Why Machine Learning Pipelines Are Important 3m
- Steps Involved to Create a Data Pipeline 3m
- Architect Scalable Data Pipelines 17m
- Best Practices for Optimized ML Systems 6m
- Case Study 1: Machine Learning Pipeline for Virtual Material Testing 11m
- Summary 2m
About the author
Abdul Rehman is the founder of Pythonist.org, a software consulting, training, and application development company. Currently, he is working as a Senior Machine Learning Engineer at Nexthon Technologies where he built several amazing projects powered with artificial intelligence. Prior to that, he worked as a Cloud Solution Architect, building powerful, secure, and scalable infrastructures on various cloud vendors like Google Cloud Platform and AWS. During both of these roles, he used Python as ... more