Machine Learning Engineering for Production (MLOps)

On June 30, 2021, Deep Learning pioneers, including Andrew Ng, the founder of DeepLearning.AI, introduced Machine Learning Engineering for Production (MLOps).

MLOps or ML Ops is a set of applications 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 field. Machine learning models are examined and improved in isolated experimental systems. When an algorithm is ready to be launched, MLOps is practiced between Data Scientists, DevOps, and Machine Learning engineers to transition the algorithm to production systems.

Similar to DevOps or DataOps approaches, MLOps seeks to increase automation and improve the quality of production models, while also focusing on business and regulatory requirements. While MLOps started as a set of best practices, it is slowly evolving into an independent approach to ML lifecycle management. MLOps applies to the entire lifecycle – from integrating with model generation (software development lifecycle, continuous integration/continuous delivery), orchestration, and deployment, to health, diagnostics, governance, and business metrics.

They expressed their opinions on many issues, especially the ones listed below;

-To what extent does the position of Data Scientist or MLE involve MLOps?
-How is MLOps actually performed in an industry setting? Is there some kind of a framework people use?
-Is MLOps suitable for early-stage startups or only teams with enough resources as the big tech companies do?
-The latest trends on MLOps and how will the future of it develop.
-What do you see as the biggest challenges for MLOps adoption?
-Apart from taking courses, what are some of the other resources or activities that might recommend to learners interested in gaining practical experience with MLOps?

The list of all speakers is as follows;

-Andrew Ng, Founder, DeepLearning.AI
-Robert Crowe, TensorFlow Developer Engineer, Google
-Laurence Moroney, AI Advocate, Google
-Chip Huyen, Adjunct Lecturer, Stanford University
-Rajat Monga, co-founder, Stealth Startup; Former lead of TensorFlow, Google
-Event moderator: Ryan Keenan, Director of Product, DeepLearning.AI

On March 24, 2021, Andres Ng shared his thoughts on Machine Learning Engineering for Production (MLOps). At the core of the subject is “From Model-centric to Data-centric AI”. There was also a Question and Answer section at the end of this event.


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