[Ajah] Speaker Image Placeholder An Introduction to Machine Learning Operations Gift Ojeabulu Data Scientist

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Who am I? - Co-founder and community lead for Data Community Africa/DatafestAfrica. - Organizer of MLOps Community Lagos meetup. - Ex Data Scientist at CBB Analytics

Learning Objective - What is MLOps ? - Why do we need MLOps ? - Devops vs MLOps - Core principles of MLOps - Benefits of MLOps - MLOps communities - Best resources to Learn MLOps - Conclusion

A prelude to MLOps.

An Overture - What is MLOps - Why MLOps. - CRISP-DM VS ASUM-DM Methodology - Devops vs MLOps - The Future of 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. What is MLOps?

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.

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.

Core principles of MLOps Continuous integration and delivery Infrastructure as code Monitoring and logging

Benefits of MLOps Improved project quality Faster time to the market Increased speed Reduced costs

MLOps Communities ● MLOps Community Lagos ● DataTalksClub ● Google Kubeflow community ● IterativeAI community

Best resources to learn MLOps ● MLOps Zoomcamp(Free) ● Iterative MLOps course(Free) ● Practical MLOps Book(Paid) ● Design Machine Learning Systems(Paid)

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. CEO of WhyLabs

Thank you! Gift Ojeabulu MLOps Lagos @GiftOjeabulu_