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Deploying Machine Learning Models to Production: Challenges & Solutions using MLOps - Adarsh Shah

For the most part Machine Learning is similar to traditional Software Development and most of the principles and practices that apply to Traditional Software Development also apply to Machine Learning. But there are also certain unique challenges that come with deploying ML models to Production. In this presentation, we will look at the top Challenges you face deploying Machine Learning Models to Production and how to tackle those Challenges using MLOps. Key Takeaways: How is Machine Learning different than Traditional Software Development Top challenges deploying ML Models to Production What is MLOps and how to tackle ML specific challenges using that Anecdotes about deploying ML Models using industry principles and best practices Adarsh Shah Adarsh Shah is an Engineering Leader, Coach, Hands-on Architect & a Change Agent with over 15 years of industry experience helping his clients as an independent consultant. He is also an organizer for Devopsdays NYC conference & devopsnyc meetup. Adarsh has a keen interest in building systems that add business value and is passionate about helping clients with digital transformation by improving both technical as well as non-technical aspects. These days, he is excited about working with Machine Learning and Cloud-Native technologies. You can reach him on twitter at @shahadarsh.