MLOps with Data Version Control: Getting Started
MLOps 2023
| Beginner
- 16 videos | 1h 51m 48s
- Includes Assessment
- Earns a Badge
Data Version Control (DVC) is a technology that simplifies and enhances data versioning and management. It provides Git-like capabilities to track, share, and reproduce changes in data while optimizing storage and facilitating collaboration in data-centric projects. In this course, you will discover how DVC simplifies the intricate components of ML projects - code, configuration files, data, and model artifacts. Next, you will embark on hands-on DVC exploration by installing Git locally and establishing a remote repository on GitHub. Then you will install DVC, set up a local repository, configure DVC remote storage, and add and track data using DVC. Finally, you will create Python-based machine learning (ML) models and track them with DVC and Git integration. You will create metafiles pointing to DVC-stored data and artifacts and commit these files to GitHub, tagging different model and data versions. Through Git tags, you will access specific model iterations for your work. This course will empower you with theoretical insights and practical proficiency in employing DVC and Git.
WHAT YOU WILL LEARN
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Discover the key concepts covered in this course
Outline key concepts of dvc
Describe the features of git
Describe the features of dvc
Install git on your local machine
Install dvc on your local machine
Create a git local repository
Connect to a remote github repo from git -
Configure a remote storage configuration in dvc
Push a file to dvc remote storage
Create and serialize an ml model
Push and commit a model to dvc and git
View the files that github tracks
Run and push a different model version
Revert to previous code versions in git
Summarize the key concepts covered in this course
IN THIS COURSE
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2m 4sIn this video, we will discover the key concepts covered in this course. FREE ACCESS
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10m 26sAfter completing this video, you will be able to outline key concepts of DVC. FREE ACCESS
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3. A Brief Overview of Git7m 49sUpon completion of this video, you will be able to describe the features of Git. FREE ACCESS
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4. DVC Concepts7m 14sAfter completing this video, you will be able to describe the features of DVC. FREE ACCESS
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5. Installing Git4m 56sLearn how to install Git on your local machine. FREE ACCESS
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6. Installing DVC7m 4sIn this video, find out how to install DVC on your local machine. FREE ACCESS
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7. Creating a Git Local Repository9m 6sDuring this video, discover how to create a Git local repository. FREE ACCESS
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8. Connecting to GitHub from Git8m 2sIn this video, you will learn how to connect to a remote GitHub repo from Git. FREE ACCESS
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9. Configuring a Remote Storage Configuration in DVC7m 10sDiscover how to configure a remote storage configuration in DVC. FREE ACCESS
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10. Pushing Files to DVC Remote Storage6m 17sFind out how to push a file to DVC remote storage. FREE ACCESS
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11. Creating a Machine Learning (ML) Model in Python8m 10sDuring this video, you will learn how to create and serialize an ML model. FREE ACCESS
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12. Pushing an ML Model to DVC and Git8m 58sIn this video, discover how to push and commit a model to DVC and Git. FREE ACCESS
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13. Viewing the Files Committed to GitHub4m 7sFind out how to view the files that GitHub tracks. FREE ACCESS
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14. Running and Pushing a Different Model Version9m 22sDuring this video, you will learn how to run and push a different model version. FREE ACCESS
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15. Reverting to Previous Code Versions in Git9m 5sDiscover how to revert to previous code versions in Git. FREE ACCESS
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16. Course Summary2mIn this video, we will summarize the key concepts covered in this course. FREE ACCESS
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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