Thread by @Saboo_Shubham_

Do you ever feel like your data science and IT teams are speaking different languages? That's where #MLOps comes in!

By standardizing workflows and processes, MLOps can bridge the gap between these two critical teams.

(A thread) 👇🧵

One of the key benefits of MLOps is that it enables data scientists and IT professionals to work together more efficiently and effectively. For example, MLOps practices can help to ensure that data scientists have access to the right infrastructure and tools.

Another important aspect of MLOps is that it enables data scientists to focus on what they do best – developing models and algorithms – while IT takes care of the operational aspects of model deployment and management.

This improved collaboration between data science and IT also helps to ensure that models are deployed in a way that is secure, reliable, and scalable. For ex, MLOps practices can help to prevent security breaches and ensure that models continue to perform well in production.

At the end of the day, MLOps practices can help organizations to achieve their AI goals more quickly and efficiently. By fostering collaboration between data science and IT teams, MLOps can help to ensure that AI is deployed in a way that is secure, reliable, and scalable.

Sounds interesting but don't know where to start?

Check out this end-to-end MLOps platform by @abacusai 👇

abacus.ai

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