MLOps – How to automate and accelerate the machine learning lifecycle?

This weekend I tried to step aside a little bit of my comfort zone to test what MLOps is all about.

First, lets understand some use cases on how Machine Learning is used across different areas. ML adoption requires a cultural shift and a technology environment with people, processes, and platforms operating in the responsive, agile way organizations are looking to operate today.

What is MLOpens?

Machine Learning Operations (MLOps) draws on DevOps principles and practices. Built upon notions of work efficiency, continuous integration, delivery, and deployment, DevOps responds to the needs of the agile business – in short, to be able to deliver innovation at scale.

How Does MLOps Benefit ML?

MLOps applies DevOps principles and best practices to ML delivery, enabling the delivery of ML-based innovation at scale to result in:

• Faster time to market of ML-based solutions

• More rapid rate of experimentation, driving innovation


View original post 293 more words

Leave a comment

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

This site uses Akismet to reduce spam. Learn how your comment data is processed.

%d bloggers like this: