Meetup #82

Basics of End-to-End MLOps

MLOps, or DevOps for machine learning, enables data science and IT teams to collaborate and increase the pace of model development and deployment by monitoring, validation, and governance of machine learning models. To understand MLOps, we must first understand the ML systems lifecycle from developing ML models to deploying and monitoring them.

Take-aways

Understand the basics steps involved in productionsing an ML Model. Have an end-to-end picture

In this episode

Raviraja Ganta

Raviraja Ganta

Founding Engineer - NLP, Enterpret

Raviraja is currently working at Enterpret as a Founding Engineer - NLP. His interests are in Unsupervised Algorithms, Semantic Similarity, and Productionising the NLP models. Raviraja likes to follow the latest research works happening in the NLP domain. Besides work, Raviraja likes cooking 🥘 , cycling 🚴‍♀️ , and kdramas 🎥.

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Demetrios Brinkmann

Demetrios Brinkmann

Host

Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.