AWS re:Invent 2023 - Introduction to MLOps engineering on AWS (TNC215)

This session introduces machine learning operations (MLOps). A lack of MLOps skills is one of the top challenges in operationalizing artificial intelligence (AI). MLOps builds upon and extends the DevOps practice prevalent in software development to build, train, and deploy ML models. Learn the similarities and differences between DevOps and MLOps practices.
Learn more about AWS re:Invent at go.aws/46iuzGv.
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[@leenaagrawal7084]

Very clear and crisp explanation , thankyou !

[@AWSEventsChannel]

We are glad to hear it was helpful for you. 🙌

[@kernelpanic3520]

At last a good explained tech talk!! thanks..

[@ilyas467]

In practice, MLOps and DevOps often work together in organizations that develop and deploy machine learning applications. MLOps can be seen as an extension of DevOps principles tailored for machine learning projects, incorporating additional considerations specific to ML workflows. By leveraging the best practices from both MLOps and DevOps, organizations can build robust, scalable, and maintainable machine learning systems.

[@krishm5116]

100% correct. Since ML was used extensively, the ML domain has introduced dedicated MLOps 🙂