Getting machine learning into production is hard — the MCubed webcast is here for support

The MCubed webcast returns this week to tackle a whole other beast: Continuous Delivery for Machine Learning. Join us on October 7th at 11am BST (that’s 12 o’clock for you CEST peeps) to get into the nitty gritty of the operational side of ML.

If you’ve ever worked with an application that uses some form of machine learning, you’ll know that some component or other is always evolving: If it isn’t the training data that’s changing, you’ll surely come across a model that needs updating, and if all is well in those areas, there’s 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 free MCubed webcast on October 7th, 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 to set up a comprehensible continuous delivery (CD) workflow for their machine learning projects.

You might know Mr Sato 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 already shared insights from working on all sorts of platform modernisation and data engineering projects, that also informed some of the good practices he recently investigated.

On the webcast, Sato 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. He’ll 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 Sato 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 7th will start 11am BST (12pm CEST) with a roundup of the latest in machine learning-related software development news, but then it’s straight on to the talk.

Don’t forget to let us know if you have any topics you’d 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 receive a quick reminder on the day — we’re really looking forward to seeing you on Thursday!