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MLOps Exploration with Git & DVC for Machine learning project on DagsHub

Who am I? - Sport Data Scientist (Basketball). - Founder of The African Data Community Newsletter. - Technical Writer, Public Speaker, Social Media Content creator, Machine Learning Thought Leader (Global AI Hub), Open-source & Community Advocate. Gift Ojeabulu CBBAnalytics @GiftOjeabulu_

Learning Objective - A prelude to MLops. - DagsHub & DVC Workflow. - A Diagrammatic explanation of Code, Model, Data parameters & metrics in respect to DVC & DagsHub. - Connect, Migrate, Create your DagsHub Repo. - A practical Guide to build your first MLops project with DVC & DagsHub. - Conclusion.

A prelude to MLOps.

An Overture - What is MLOps - Why MLOps. - CRISP-DM VS ASUM-DM Methodology - MLops tools Categorization/class. - The Future of MLOps.

Open Source Festival 2022 What is MLOps? MLOps is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently. The word is a compound of "machine learning" and the continuous development practice of DevOps in the software development field.

Open Source Festival 2022 Why MLOps? MLOps is a set of practices for collaboration and communication between data scientists and operations professionals. Applying these practices increases the quality, simplifies the management process, and automates the deployment of Machine Learning and Deep Learning models in large-scale production environments.

Open Source Festival 2022 The Future of MLOps MLOps is the future of machine learning, and it brings a host of benefits to organizations looking to deliver high-quality models continuously. It also offers many other benefits to organizations, including improved collaboration between data scientists and developers, faster time-to-market for new models, and increased model accuracy.

DVC is to Machine learning Engineers what Git is to Software Engineers.

Photo comes here DVC & DagsHub Workflow Data Version Control) is an ideal tool for versioning your binary data. However, you cannot view the DVC tracked files on GitHub.Wouldn’t it be nice if there is a platform that is like GitHub, but is more optimized for data scientists and machine learning engineers? That is when DagsHub comes in handy Git - DVC - DagsHub

DagsHub is to Machine learning Engineers what GitHub is to Software Engineers.

Photo comes here Code, Model, Data parameters & metrics As a Data Scientist you might want to version your code, model, data and metrics so you can reproduce a certain experiment. Github is a great platform to version control your code but not ideal to version your data, model and metrics. DagsHub is ideal. DagsHub is Github Supplement

Open Source Festival 2022 Connect your Repo If you want to manage your repository through both GitHub and DagsHub, Connect A Repo.

Migrate your Repo If you already have a repository on GitHub and you want to migrate your repository to DagsHub, Migrate A Repo.

Create your Repo DagsHub allows you to either create a new repository on their platform or connect to a repository on GitHub. If you want to create a new repository on DagsHub, click New Repository.

What Next?

Go ye therefore & build your first MLOps project with DVC & DagsHub 1. Khuyen Tran Article 2. Abid Ali Awan Article. 3. Dagshub Documentation

In Conclusion

Alessya Visnjic Data Scientist need to think about their models in post-production because only when the model is in production is when it starts generating value. What the CEO of Whylabs said at DataFramed Podcast about MLOps…

Thank you! Gift Ojeabulu CBB Analytics @GiftOjeabulu_

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