DeepOps: Building an AI First Company

Abstract: Data scientists spend 30% of their time building shoddy infrastructure. Our data shows that many AI teams can accelerate their progress by 10x at least. Deep Learning brings with it enormous amounts of data, complicated experiment results and intense compute requirements. Decades of experience in moving code to production yielded best practices in engineering that have not yet found their place in deep learning teams. Breaking silos to foster trust, a transparent culture, and shared responsibility - we introduce DeepOps - deep learning ops. A set of methodologies, tools and culture where data engineers and scientists collaborate to build a faster and more reliable deep learning pipeline.
Surveying hundreds of AI companies, we've learned that adopting DeepOps practices helped them ship faster, with more confidence and improved customer experiences.
In this talk, Yuval Greenfield, Deep Learning Developer Relations at, will discuss:
DeepOps checklist - insights from leading AI teams and how to bring them to your team.
Increase productivity within data science teams
Reduce time to market
Increase deployment speed
Increase visibility and transparency across teams.

Bio: Yuval Greenfield has been an engineer and data enthusiast for the past 13 years in the fields of military cybersecurity, computer vision medical diagnostics, gaming, 360 cameras, and deep-learning tools. He holds a B.Sc. in Physics and Mathematics from the Hebrew University of Jerusalem as part of the IDF Talpiot program. At MissingLink, Yuval is in charge of developer relations, using the MissingLink platform for deep learning research, building tutorials, marketing content, and technical presentations.