Sep 9–12, 2019

Are we deployed yet? Turning AI research into a revenue engine

Manasi Vartak (
2:35pm3:15pm Thursday, September 12, 2019
Location: 230 B
Secondary topics:  Machine Learning
Average rating: ***..
(3.50, 2 ratings)

Who is this presentation for?

  • Data scientists, machine learning engineers, data science managers, R&D, and VPs 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 for 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 reimplementing 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, has 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, Manasi Vartak has found common practices and tools that are crucial to productionizing AI/ML models. She examines 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. She also demonstrates how these practices can equip data-driven enterprises to cross the chasm between research and production.

Prerequisite knowledge

  • A basic understanding of ML
  • Familiarity with frameworks used for ML and use cases where ML could be applied in your business or product
  • Experience deploying production models (useful but not required)

What you'll learn

  • Understand why putting ML into production is so hard and what you can do about it
  • Discover tools (open source and proprietary) that you can use
  • Learn what business impact you can demonstrate by operationalizing ML
Photo of Manasi Vartak

Manasi Vartak

Manasi Vartak is the founder and CEO of, 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 used at Fortune 500 companies and in popular open source projects including Kubeflow. Manasi earned her PhD 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 newsfeed, automated data visualization, and ML model debugging. She’s a recipient of the Facebook PhD Fellowship and the Google Anita Borg Scholarship.

  • Intel AI
  • O'Reilly
  • Amazon Web Services
  • IBM Watson
  • Dataiku
  • Dell Technologies
  • Intuit
  • Gamalon
  • Hewlett Packard Enterprise
  • MapR Technologies
  • Sisu Data
  • Intuit

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