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Adarsh Shah on "Continuous Delivery for Machine Learning"

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Hosted By
Tom L. and russ k.


Note: This is a virtual event. Please use the zoom link below:

5:00pm - Talk + Q&A
5:45pm - Socialize

Title: Continuous Delivery for Machine Learning

Speaker: Adarsh Shah

Continuous Delivery has been a key approach for deploying changes for Traditional Software to Production safely and quickly in a sustainable way.

Machine Learning (ML) is fundamentally different than Traditional Software. Typical ML workflow includes Data Management, Experimentation (Model Training & Development), Model Deployment, and Prediction. Training a model takes hours & sometimes days & typically deals with a large dataset. Training & Model Prediction also requires special resources like high-density cores & GPUs. Due to these reasons & others, ML systems have their own challenges deploying to Production.

In this presentation, we will look at those top challenges deploying ML systems to Production and how Continuous Delivery Principles can help solve those challenges so that ML systems can also be deployed safely and quickly in a sustainable way to Production. We will also be looking at different tools available to enable Continuous Delivery for Machine Learning.

Speaker Bio:

Adarsh Shah is an Engineering Leader, Coach, Public Speaker, Hands-on Architect & a Change Agent. He is also an organizer for Devopsdays NYC conference & devopsnyc meetup. Adarsh has a keen interest in building systems that add business value. He is an independent consultant passionate about helping clients with Software Architecture/Development, Leadership Enablement/Coaching & DevOps/Cloud needs by looking at both technical as well as non-technical aspects. These days, he is excited about working with Machine Learning and Cloud-Native technologies. Find out more about Adarsh at or reach him on twitter at @shahadarsh.

In his spare time, he enjoys playing cricket, traveling, and trying new whiskies.
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