“87% of data science projects will never make it to production”, says VentureBeat. The process of developing, deploying and continuously improving Machine Learning applications is complex and requires a wide array of tools and infrastructure. We can apply the principles of Continuous Delivery to get more models safely into production. The tools and technologies for ML systems are different from mainstream software but core DevOps principles are still effective for ML. Join us to understand how CD4ML applies continuous delivery practices to developing Machine Learning models so that they are always ready for production. We’ll learn how this allows small and safe incremental changes that can be reproduced and released by cross functional teams.[color=#111111][color=#111111][size=2][font=Verdana, Arial, Helvetica, sans-serif][size=2][font=Verdana, Arial, Helvetica, sans-serif][h2][/font][/size][/font][/size][/color][/color][b][color=#111111][size=2][font=Verdana, Arial, Helvetica, sans-serif][color=#111111][size=2][font=Verdana, Arial, Helvetica, sans-serif][url=https://drive.google.com/file/d/1o3PEu2iPuETQB8Fgf4L7UMdwHVbny_pv/view?usp=sharing]Slides[/url][/font][/size][/color][/font][/size][/color][/b][color=#111111][color=#111111][size=2][font=Verdana, Arial, Helvetica, sans-serif][size=2][font=Verdana, Arial, Helvetica, sans-serif][/h2][/font][/size][/font][/size][/color][/color]