This course will teach you the aspects to understand MLOps journey, end to end data quality checks and establish the mechanism of data cataloging, principles around metadata management and data governance.
Data quality is an important prerequisite prior to machine learning modelling. It is of utmost importance to thoroughly assess data quality before model building. In this course, Principles for Data Quality Measures, you’ll learn to build MLOps pipelinse and explore best practices for metadata management. First, you’ll explore data discovery and cataloging. Next, you’ll discover data profiling and quality checks. Finally, you’ll learn to explore data lineage and the best metadata management practices and analyze the MLOps cycle. By the end of this course, you’ll gain a better understanding of data discovery, profiling, and metadata management of the ML Model building process.
Course Overview Hi, everyone. My name is Niraj Joshi. Welcome to my course, Principles for Data Quality Measures. I'm a cloud machine learning architect. Data quality is an important prerequisite prior to machine learning modeling. It's of prime importance to thoroughly assess data quality before model building. This course will introduce you to principles of data governance, benefits of data lineage, and metadata management. Some of the major topics that we'll cover include benefits of data profiling, reason to implement data lineage, importance of data governance policy, and metadata management of the ML model. By the end of this course, you will know the overview of data discovery, profiling, and metadata management of the ML model building process. I hope you will join me on this exciting journey to learn Principles for Data Quality Measures course, at Pluralsight.