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.

Citación :

https://rapidgator.net/file/0d8ca5e61168c474b89481b85328b718/s1c28.Principles.for.Data.Quality.Measures.rar.html


https://nitroflare.com/view/2B0B1D292D8D56B/s1c28.Principles.for.Data.Quality.Measures.rar


https://uploadgig.com/file/download/732A5a48f458ae99/s1c28.Principles.for.Data.Quality.Measures.rar