Continuous Delivery for Machine Learning Systems – All Day DevOps
References mentioned in the presentation:
- Dr. Deming’s 14 Points for Management
- Challenges Deploying Machine Learning Models to Production
- State of DevOps Report
- Large image datasets: A pyrrhic win for computer vision?
Description: Machine Learning workflow includes data management, experiment management (model training & development), model deployment, serving, and retraining. Training a model takes hours & some times days & typically deals with a large dataset. Training & serving a model also require special resources like high-density cores & GPUs.
In this talk, we will look at how Continuous Delivery for Machine Learning looks like using anecdotes and how to use cloud-native technologies to perform various steps in a Machine Learning workflow. We will also be talking about how it is different from deploying other software and what are the various aspects to consider. We will also be looking at different tools available to enable Continuous Delivery for machine learning.