The Fifth Elephant 2019

Gathering of 1000+ practitioners from the data ecosystem

Tickets

Machine Learning Platform @Flipkart

Submitted by Naresh Reddy Sankapelly (@nareshsankapelly) on Thursday, 6 June 2019

Session type: Full talk of 40 mins

Abstract

Every decision at Flipkart is data driven which implies every team at Flipkart is adopting Machine Learning based solutions. Machine Learning Platform enables data scientists and engineers to build, productionize and monitor machine learning models reliably at scale.
In this talk, we will walk you through the challenges faced in building ML Platform and evolution of the platform. We will also cover why it was important for us to make ML Platform a part of Flipkart’s Data Platform and how it helped us in building capabilities like interactive experimentation, batch prediction, model reproducibility and model health monitoring.

Outline

  • Why did we build Machine Learning Platform at Flipkart?
  • Challenges faced in productionizing ML models and how did we solve them.
  • MLPlatform Evolution.
  • How we leveraged Flipkart’s Data Platform to enable interactive experimentation, batch prediction, model reproducibility and model health monitoring?
  • Future Work

Speaker bio

Engineer working on Machine Learning Platform at Flipkart. Before that, he worked at VizExperts on GIS, GPU Computing and Computer Graphics. He graduated with B.Tech in Computer Science from IIIT Allahabad.

Links

Slides

https://www.slideshare.net/NareshSankapelly/machine-learning-platform-flipkart-slash-n-conference-2018

Comments

  • Abhishek Balaji (@booleanbalaji) Reviewer 6 months ago

    Hi Naresh,

    Thank you for submitting a proposal. We need to see detailed slides and a preview video to evaluate your proposal. Your slides must cover the following:

    • Problem statement/context, which the audience can relate to and understand. The problem statement has to be a problem (based on this context) that can be generalized for all.
    • What were the tools/frameworks available in the market to solve this problem? How did you evaluate these, and what metrics did you use for the evaluation? Why did you pick the option that you did?
    • Explain how the situation was before the solution you picked/built and how it changed after implementing the solution you picked and built? Show before-after scenario comparisons & metrics.
    • What compromises/trade-offs did you have to make in this process?
    • What is the one takeaway that you want participants to go back with at the end of this talk? What is it that participants should learn/be cautious about when solving similar problems?

    We need your updated slides and preview video by Jun 15, 2019 to evaluate your proposal. If we do not receive an update, we’d be moving your proposal for evaluation under a future event.

  • Naresh Reddy Sankapelly (@nareshsankapelly) Proposer 5 months ago (edited 5 months ago)

    Hi Abhishek,

    I have shared slides and a video link from a previous talk on the same topic. I will update the links asap.

    • Abhishek Balaji (@booleanbalaji) Reviewer 5 months ago

      Hi Naresh, I went through the updated content and here’s the feedback:

      1) We have received a bunch of proposals on Machine learning platforms, especially on the journey of building one at large companies. This creates a problem where all these platforms are novel for your use case.

      2) The problems in building machine learning platforms are common and the topic is pretty niche for the breadth of audience at The Fifth Elephant.

      I am moving the proposal for evaluation under a future event where a smaller audience would find the talk very useful. We’re also putting together a Birds of a Feather session on building machine learning platforms and will keep you updated on the same.

  • Abhishek Balaji (@booleanbalaji) Reviewer 5 months ago

    Thanks, will move this to evaluation.

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