DevOps for Machine Learning

Explore the intersection of DevOps and machine learning in this comprehensive conference talk. Learn how to coordinate data science and software engineering teams effectively using DevOps principles. Discover strategies for building a robust end-to-end delivery pipeline that integrates data acquisition, preparation, experimentation, and model training with software development processes. Gain insights into model source control, repeatable data preparation, continuous retraining, code validation, testing, model storage, versioning, and production deployment. Understand how to overcome the challenges of introducing data science workflows into traditional software engineering environments and create a cohesive, automated pipeline that keeps all teams in sync. Equip yourself with the knowledge to implement DevOps practices in machine learning projects, fostering collaboration between data scientists, software engineers, and IT operations for successful smart software development.

DevOps for Machine Learning - Damian Brady