Principles for Data Quality Measures
- Offered byPluralsight
Principles for Data Quality Measures at Pluralsight Overview
Duration | 1 hour |
Total fee | Free |
Mode of learning | Online |
Schedule type | Self paced |
Difficulty level | Advanced |
Official Website | Explore Free Course  |
Credential | Certificate |
Principles for Data Quality Measures at Pluralsight Highlights
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Principles for Data Quality Measures at Pluralsight Course details
- 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.
Principles for Data Quality Measures at Pluralsight Curriculum
Course Overview
Course Overview
Introducing Data Discovery and Cataloging
Introduction to the Course
Types of Machine Learning
Key Metrics to Assess Data Quality
Purpose of Data Cataloging
Evaluating Data Quality and Profiling
Benefits of Data Profiling
Domain Specific Data Quality Checks
Feature Engineering Pipeline
Demo: Data Profiling
Summary
Tracking Data Lineage and Governance
Introduction to Data Governance
Benefits of Data Lineage and Governance
Prerequisites to Train ML Model
Training the ML Model
Versioning ML as a Service
Summary
Exploring Best Practices for Metadata Management
Overview of MetaData Management
Effective Metadata Management Practices
MLMD Database
Demo: Assess Metadata of ML Model
Summary
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