Making Data Science Work session 2

On Model Productionization


Despite growing teams and budgets, very few machine learning models reach the production stage.

In this forum, hosts Venkata Pingali and Indrayudh Ghoshal of Scribble Data will chat with Aditya Patel, Director, Data Science of Glance (InMobi; Bangalore) and Nischal HP @nischalhp, VP of Technology, Omnius (Berlin), discussing:

  1. What is model productionization? What makes it difficult?
  2. How does it relate to MLOps?
  3. How should one plan for production ML?
  4. What kinds of systems and process should be in place for ensuring successful delivery?
  5. What does the team structure and skillset look like?

1. Uncertainty cannot be eliminated in the performance of the model. So extensive testing, circuit breakers, fallback mechanisms, and incremental deployment mechanisms are critical.
2. ML development and deployment should be thought about within the context of the product development.
3. Many of the systems, methods and ideas are applicable here including versioning but need to be repurposed to suit the context

Ask questions, post comments for the speakers and hosts on the Comments section.

Watch the YouTube recording of the livestream

Previous session: The first session was held on 20 May. Summary of the session is available here:

About the hosts: Scribble Data is a Bangalore/Toronto startup, active in the data community. Scribble implements MLOps for Data using their feature store, Enrich. This enables data science teams to train models faster, and with confidence in the underlying data.

About the organizers: The Fifth Elephant is a platform for practitioners working with data (anywhere from ingestion, to its application in data science for different use cases) to showcase their work and to collaborate.

For further inquiries, contact 7676332020 or write to


Scribble Data