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.
Author Name: Niraj Joshi
Author Description:
Niraj is a AWS/Azure DevSecOps Cloud Specialist with over a decade of work experience into Data Modeling with Databases like Cassandra, MongoDB, SparkSQL, ElasticSearch and SQL Server. He has over 7 years of work ex into Computer Vision, Artificial Intelligence, DevOps, Machine Learning and Big Data Stack, he has been a consultant to companies like CISCO, ERICSSON, Dynamic Elements and JP Morgan He has excellent data visualization/ analytics skills and quite proficient in languages like Python ,… more