Some Failures and Lessons Learned Using AI in our AI company
Some Failures and Lessons Learned Using AI in our AI company


We started on an exciting journey for AI-driven Engineering to apply AI in our own company. With enthusiastic developers, nearly limitless cloud resources, and a magical AI product, what could possibly go wrong?
Turns out, a lot.
Borys and Dustin, who lead the Engineering Productivity domain within DataRobot, will tell you some stories of how they use the DataRobot AI platform to improve R&D operations and efficiency.

Session Outline
The exciting promise of AI-driven engineering (10 mins)
Use case: Triaging JIRA tickets using AI (10 mins)
Use case: Optimizing cloud compute resource allocation by predicting container sizes (15 mins)
Lessons learned (20 mins)
Q&A (Last 15 mins of 75 mins slot)


Since the start of my career 15 years ago, I’ve explored various aspects of how humans and machines can work together - AI / Machine Learning, human-computer interaction, semantic reasoning, etc. At first it was about the technology and how to advance its capability, but has shifted more towards the role of humans and how to maximize their performance within a complex system of automation and AI. As an early employee at DataRobot, I’ve had the opportunity to lead and grow many teams - Release, QA, Test Automation, Developer Experience, Developer Enablement and Engineering Productivity - and am passionate about enabling developers to succeed. I love coding, breaking things, and making things more usable.

Open Data Science




Open Data Science
One Broadway
Cambridge, MA 02142

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