Version-Control-for-Models

Version control for models is an essential practice in data science and machine learning, enabling teams to track and manage changes to their models over time. Just as software developers use version control systems like Git to manage code, data scientists need similar tools to handle the complexities of model development. This includes tracking different versions of models, configurations, and datasets, which is crucial for reproducibility and collaboration. By implementing version control, teams can easily identify changes, roll back to previous versions if needed, and ensure that all members are aligned, ultimately leading to more efficient workflows and improved model performance.

Version Control Your ML Model Deployment With Git using Modelbit

 Towards Data Science

Develop, deploy, and track! Photo by Yancy Min on Unsplash Introduction Version control is critical to all development processes, allowing developers to track software changes (code, configurations, ...

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What is version control

 Atlassian Git Tutorial

Version control, also known as source control, is the practice of tracking and managing changes to software code. Version control systems are software tools that help software teams manage changes to ...

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The DVC Guide: Data Version Control For All Your Data Science Projects

 Towards Data Science

As data scientists, we experiment with different versions of code, models, and data. Additionally, we even use version control system like Git to manage our code, track versions, move forward and…

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Data Versioning for Efficient Workflows with MLFlow and LakeFS

 Towards AI

Version Control Systems, such as Git, are essential tools for versioning and archiving source code. Version Control helps you keep track of the changes in the code. When a change is made, an error…

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Getting Started With Version Control

 Level Up Coding

Version Control is an absolutely critical component to developing that every coder should be familiar with. Version Control is a logging system which records the history of your code so that the…

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Introduction to DVC: Data Version Control Tool for Machine Learning Projects

 Towards Data Science

Git is a powerful tool for version control. It allows you to go back and forth between different versions of your code without being afraid of losing the code you change. As a data scientist, you…

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Overview of GIT Version Control System

 Analytics Vidhya

In software engineering, version control (also known as revision control, source control, or source code management) is system responsible for managing changes to computer programs, documents, large…

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Version Control or Die

 Level Up Coding

As a programmer, one thing I’ve learned the hard way is that version control is not just a nice-to-have — it’s a necessity. In fact, I would go so far as to say, “Version Control or Die.” That might s...

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Model Rollbacks Through Versioning

 Towards Data Science

The Walmart Rollback isn’t the only kind that can save you money Using Model Rollbacks Is Fun! There’s general consensus in the Machine Learning community that models can and have made biased decisio...

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The way you version control your ML projects is wrong

 Towards Data Science

How can you version the slightest change you make inside your project, automatically, without even thinking about it.

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Versioning data and models in ML projects using DVC and AWS S3

 Analytics Vidhya

We will be looking at how DVC can be used to version our data and models in this blog in detail. The code for this blog is available here. For details regarding the model training for Named Entity…

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Data and Machine Learning Model Versioning with DVC

 Towards Data Science

DVC: It’s a Git, but for Our Data and ML Model Continue reading on Towards Data Science

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