Keeping an Eye on AI

Your machine learning model is trained and finally running in production. But that was the easy part. Now, the real challenge is reliably running your machine learning system in production. For this, monitoring systems are essential. But while monitoring machine learning models, you must consider some challenges that go beyond traditional DevOps metrics.

Tools & Processes for MLOps

Training a machine learning model is getting easier. But building and training the model is also the easy part. The real challenge is getting a machine learning system into production and running it reliably. In the field of software development, we have gained a significant insight in this regard: DevOps is no longer just nice to have, but absolutely necessary. So why not use DevOps tools and processes for machine learning projects as well?

Continuous Delivery for Machine Learning

In modern software development, we’ve grown to expect that new software features and enhancements will simply appear incrementally, on any given day. This applies to consumer applications such as mobile, web, and desktop apps, as well as modern enterprise software. We’re no longer tolerant of big, disruptive software deployments. ThoughtWorks has been a pioneer in Continuous Delivery (CD), a set of principles and practices that improve the throughput of delivering software to production in a safe and reliable way.

Behind the Tracks