Are we deployed yet? Turning AI research into a revenue engine
Who is this presentation for?Data Scientists; Machine learning engineers; Data Science Managers; R&D; VP of Data Science
Enterprises are making large investments in integrating AI/ML into their business, and yet it remains challenging to transform these initiatives into core, revenue driving functions.
The key reason of this gap is that AI and ML have traditionally been research initiatives dominated by prototypes and proof-of-concepts, whereas operationalizing AI requires packaging, testing, setting up data pipelines, and enabling monitoring — areas squarely outside the scope of a researcher. As a result, research models often get shipped to a software engineering team tasked with re-implementing the model without proper context. It therefore takes many weeks to even months to deploy a model into production.
As creators of ModelDB, an open-source model management solution developed at MIT and the proprietary Verta platform, we have managed and deployed several hundreds of models ranging from cutting-edge deep learning models for autonomous driving, to traditional ML models in finance and retail. Across these diverse application areas, we have found common practices and tools that are crucial to productionizing AI/ML models. In this talk, we describe key processes and tools by which enterprise AI teams can leverage and think about model data pipelines, A/B testing, reproducibility, and interpretation to drive revenue. We demonstrate how these practices can equip data-driven enterprises to cross the chasm between research and production.
Prerequisite knowledge- Basic Understanding of ML and familiarity with frameworks used for ML. - Use cases where for ML could be applied in your business or product. - Passion to use ML in products to move your company forward. - Previous experience with deploying production models a plus.
What you'll learn- Why is putting ML into production so hard? Is it only for the Ubers and Facebooks of the world? - What can you do about it? - What tools (open-source and proprietary) can you use? - What business impact can you demonstrate by operationalizing ML?
Manasi Vartak is the founder and CEO of Verta.AI an early-stage startup building software to help data science and machine learning teams rapidly build and integrate ML across products. Manasi is the creator of ModelDB, the first open-source model management system that is used at Fortune 500 companies and in popular open-source projects including KubeFlow. Manasi earned her Ph.D. in computer science from MIT CSAIL where she worked on software systems for data science and ML. Besides ML Infra, Manasi has worked on personalizing the Twitter News feed, automated data visualization, and ML model debugging. She is a recipient of the Facebook Ph.D. Fellowship and the Google Anita Borg Scholarship.
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