Join our MCubed web lecture this week to find out how to get machine learning into production – The Register

Special series If youve ever worked with an application that uses some form of machine learning, youll know that some component or other is always evolving. If it isnt the training data thats changing, youll surely come across a model that needs updating, and if all is well in those areas, theres a good chance a feature request is waiting for implementation so code modifications are due.

In regular software projects, we already know how to automatically take care of changes and make sure that we have a way of keeping our systems up to date without (too many) manual steps. The number of variables at play in ML however make it really tricky to come up with similar processes in that discipline, which is why it is often cited as one of the major roadblocks in getting machine-learning-based applications into production.

For the second episode of our MCubed webcast, on October 7, we therefore decided to sit down with you and have an in-depth look at how to tackle the operational side of ML. Joining in will be DevOps and data expert Danilo Sato, who helped quite a few organisations set up a comprehensible continuous delivery (CD) workflow for their machine-learning projects.

You might know Danilo from a popular article series on CD4ML, however his work reaches far beyond that. In his 2014 book DevOps in Practice: Reliable and Automated Software Delivery, he shared insights from working on all sorts of platform modernisation and data engineering projects.

On the webcast, Danilo will discuss how the principles of Continuous Delivery apply to machine-learning applications, and walk you through the technical components necessary to implement a system that takes care of CD for your ML project. Hell walk you through the differences between MLOps and CD4ML, take a closer look at the peculiarities of version control and artifact repositories in ML projects, give you some tips on what to observe, and introduce you to the many different ways a model can be deployed.

And in case you have all of this figured out already, Danilo will provide a look into the future of machine-learning infrastructure as well as give you some food for thought on open challenges such as explainability and auditability.

The MCubed webcast on October 7 will start 11am BST (noon CEST) with a roundup of the latest in machine-learning-related software development news, and then its straight on to the talk.

Dont forget to let us know if you have any topics youd like to learn more about, or if you are interested in practical experience reports from specific industries we really want to make these webcasts worth your time, so every hint helps. Also, reach out if you want to share some tricks yourself, we always love to hear from you!

Register here to get a quick reminder on the day were really looking forward to see you on Thursday.

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Join our MCubed web lecture this week to find out how to get machine learning into production - The Register