Architecting MLOps: Doctrine's Path to Success

Artificial Intelligence is at the core of Doctrine’s business. Over the years, they have developed over 30 Machine Learning models that are still running in production today. Two years ago, they realized that iterating over their models was becoming slow and cumbersome, mainly because of technical debt and a lack of automation. To solve this challenge, they assembled a task force of three engineers: David Huang, Ysé Wanono, and Aïmen Louafi. Over the span of a year and a half, they gathered the needs of Machine Learning Engineers, benchmarked tools, and then implemented a new architecture covering data labeling, exploration, hyperparameters tuning, experiment tracking, artefacts management, deployment, and monitoring.

⏰ Schedule

• 18h30 - 19h00: Welcome & Introduction 👋
• 19h00 - 19h45: Talk 🎙
• 19h45 - 20h00: Q&A Session 🙋‍♀️
• 20h00 - 22h00: Cocktail & Networking 🥂

🎙 About the speakers

David Huang is Machine Learning Engineer at Doctrine. During his career he has used Machine Learning on a range of topics: time series, images and, particularly at Doctrine, text. David is also a co-organizer of the Paris NLP meetups.

Ysé Wanono is Data Scientist and Data Engineer at Doctrine with previous experiences in statistical modeling at both consulting and tech startups. Ysé has been with Doctrine for more than 4 years, and for the past 2 years her focus has been on improving their MLOps lifecycle.

Aïmen Louafi is Machine Learning Engineer at Doctrine. He's fond of Computer Science, Mathematics, Machine learning. In the past, he had the opportunity to work with Graph Algorithms, OCR, Vector Search Engines, and much more.

🙌 Sponsors

Doctrine is a legal intelligence platform that uses technology to make legal analysis more credible.

nibble is the creator of spice, a Feature Platform for Machine Learning. We are also the organizers of these meetups :)

🤩 Audience

This is a meetup for data professionals who want to push forward the productionization of machine learning development within their organizations: data scientists, data engineers, software and devops engineers, data project/product managers, product designers, etc..

👋 Contact

For any inquiries, please contact florent@mlops.paris