Automate your machine learning application with docker and Jenkins
Machine Learning applications are becoming popular in our industry, however the process for developing, deploying, and continuously improving them is more complex compared to more traditional software, such as a web service or a mobile application. They are subject to change in three axis: the code itself, the model, and the data. Their behaviour is often complex and hard to predict, and they are harder to test, harder to explain, and harder to improve.
Continuous Delivery for Machine Learning (CD4ML) is the discipline of bringing Continuous Delivery principles and practices to Machine Learning applications.
Watch this webinar and find out how to automate the end-to-end lifecycle of Machine Learning applications with Docker and Jenkins to achieve speed, reliability, versioning and security.
Software Consultant at Knoldus Inc.