What is MLOps, Why do you need it, and Where do you begin

Presented by Manasi Vartak, Founder & CEO at Verta.
Building models has become easy. A few lines of code with fast.ai, huggingface or scikit-learn and you're on your way. Getting these models integrated into a product and running smoothly? A completely different ballgame. Typically this involves a dozen hacky scripts, many Jupyter notebooks, hundreds of Slack messages, and some production incidents.
All innovative AI companies today rely on a set of tools and processes to tame this chaos and ship AI products faster. Referred to as MLOps (Machine Learning Operations), these tools and processes support key needs of the ML operations lifecycle such including versioning (or experiment management), packaging, testing, deployment, and monitoring. Join this talk to learn about what is MLOps, why your organization needs it, and how to start implementing MLOps.

[@aniljain2207]

this is awesome, explaining each components of MLOPS with a demo and that too well articulated.

[@yogeshrajput9805]

Thank you very much for sharing the information about MLOps, Helpful to understand the Why MLOps

[@aikutech]

Amazing and comprehensive explanation. Thank you for this video!

[@mahdiamrollahi8456]

Is git a good tool for versioning the dataset? So, suppose that we are using manual labeling for a dataset and in each step we come to conclusion that we need to change the training dataset.

[@dhanhle6541]

Hello, Could you put the link of code source in chat ?

[@Animania31]

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