DevOps for Machine Learning

Explore the intersection of DevOps and machine learning in this comprehensive conference talk. Learn how to effectively coordinate data science and software engineering teams using DevOps principles. Discover strategies for implementing a robust end-to-end delivery pipeline that incorporates data acquisition, model training, testing, and deployment. Gain insights into source control for models, repeatable data preparation, continuous retraining, code validation, model versioning, and production deployment. Understand the challenges of integrating unfamiliar data science workflows with traditional software engineering practices, and learn how to overcome them. Delve into topics such as artificial intelligence, innovation vs. stability, experimentation, continuous integration, and monitoring. By the end of this talk, you'll have a clear understanding of how to create a cohesive, automated pipeline that brings data scientists and software engineers together for more effective smart software development.

Introduction
Machine Learning
Artificial Intelligence vs Machine Learning
Why DevOps
Innovation and Stability
DevOps Definition
Experimentation
Software Development
Planning
Source Control
Continuous Integration
Monitoring and Learning
Pipeline
Examples
Summary