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Note: This is a virtual event. We will share the conference call details closer to the event.
AGENDA:
5:00pm - Talk + Q&A
5:45pm - Socialize
Title: Continuous Delivery for Machine Learning
Speaker: Adarsh Shah
Abstract:
Continuous Delivery has been a key approach for deploying changes for Traditional Software to Production safely & 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, & 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 & how Continuous Delivery Principles can help solve those challenges so that ML systems can also be deployed safely & 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 & Cloud-Native technologies. Find out more about Adarsh at https://shahadarsh.com or reach him on twitter at @shahadarsh.
In his spare time, he enjoys playing cricket, traveling, & trying new whiskies.
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