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.