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Introduction to Data Versioning with DVC
4.8(396)3k studentsINTERMEDIATEEnglish
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Course Overview
Versioning datasets and machine learning artifacts requires specialized tools beyond traditional source control. This intermediate course covers Data Version Control (DVC), guiding you through data caching, remote storage integration, DAG-based ML pipeline automation, and experiment evaluation.
Who Should Enroll?
Designed for intermediate data scientists, ML engineers, and developers looking to bring systematic version control and pipeline tracking to machine learning projects.
Key Takeaways
- DVC Setup & Caching: Initialize DVC repositories, configure .dvcignore, and manage staging and local caches.
- Remote Synchronization: Configure Git and DVC remotes to push, pull, and checkout specific data versions.
- Pipeline DAGs & Metrics: Build modular ML pipelines, execute DAG stages, and track evaluation metrics and plots.
About this course
This course offers a comprehensive introduction to Data Version Control (DVC), a tool designed for efficient management and versioning of machine learning data. You will get an understanding of the machine learning product lifecycle, differentiating data versioning from code versioning and exploring DVC’s features and use cases.
Exploring DVC features
You will understand the motivations behind data versioning, the machine learning lifecycle, and DVC’s distinct features and use cases. You will also learn about DVC setup, covering installation, repository initialization, and the .dvcignore file. You will explore DVC cache and staging files, learn to add and remove files, manage caches, and understand the underlying mechanisms. You will learn about DVC remotes, explain the distinction between DVC and Git remotes, add remotes, list them, and modify them. You will learn to interact with remotes, push and pull data, check out specific versions, and fetch data to the cache.Automate and evaluate
You will be motivated to automate ML pipelines, emphasizing modularization of code and the creation of a configuration file. You will be introduced to DVC pipelines as directed acyclic graphs, with hands-on experience in adding stages and their inputs and outputs. You will practice executing these pipelines efficiently to enable different use cases in machine learning model training. The course concludes with a focus on evaluation, showcasing how metrics and plots are tracked in DVC.Advertisement