Practicing MLOps in your organization: tools, frameworks and governance


MLOps 2022 conference will bring together Machine Learning (ML), data engineering and SRE and AI practitioners to better understand the art of implementing MLOps in organizations.

MLOps is based on similar principles of DevOps, which talks about Continuous Delivery (CD) of ML models in production. We have seen significant traction for MLOps in recent years. Companies have been built around organizing and optimizing MLOps. We will focus on the tools, frameworks, data governance, decision-making process and approach based on real-world experience that have led to success and war stories.

The topics that we will focus in this conference include:

  1. How do you add value to your MLOps processes?
    • What are the key performance indicators?
    • How do you quantify the impact of MLOps in your organisation?
  2. What is the value addition to the business?
    • How do you measure the Return of Investment (RoI)?
    • What is the thriving work culture in your team/company?
  3. How to design cost-effective pipelines?
    • Are you investing in making the models leaner - distillation, quantization, pruning etc?
    • What is the comparison of your hardware and performance costs?
  4. What are the common mistakes made and lessons learnt in implementing MLOps?
    • How did you resolve and recover from the mistakes made?
    • Are you meeting SLA requirements and doing inference cost analysis?
  5. What are some of your favourite tools for implementing MLOps?
    • What are the key factors in deciding on a specific framework?
    • When do you choose to build your own tool?
  6. As an experienced MLOps practitioner, what advice do you provide?
    • What advice would you give for beginners who are starting on MLOps?
    • How do you scale your existing MLOps infrastructure?

In-person conference: MLOps 2022 will be held on Friday, 11 November, at Microsoft Reactor, Lavelle Road, Bangalore. This is an in-person conference. Register to participate for the conference.

Code of Conduct: Hasgeek’s Code of Conduct applies to the MLOps 2022 conference - participants, speakers and sponsors.

COVID protocols and masking policy: In keeping with COVID protocols, the following is applicable to all participants:

  1. Participants attending in person must keep their vaccination certificate handy. The venue will ask you to show your vaccination certificate as proof of being fully vaccinated.
  2. Wearing masks is optional.

Contact information: For queries about the conference, contact Hasgeek at or call (91)7676332020.

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Featured submissions

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  • Zainab Bawa

    Zainab Bawa

    Kannan S Editorial Assistant

    Nischal HP

    Nischal HP Editor

    MLOps 2022 conference report

    Context This is a report of the MLOps 2022 conference, held on 11 November, at the Microsoft Reactor, Bangalore. more

    30 Nov 2022


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ML at scale: how Udaan built its ML platform

ML at scale: how Udaan built its ML platform

Sai Sharan Tangeda, Mohit Kumar, Sampan Nayak - ML platform team at Udaan

38 minutes11 November 2022
Make AI real - from labs to production

Make AI real - from labs to production

Ravi Kumar Meduri, Executive Vice President at Innominds

34 minutes11 November 2022
Panel: Building value to MLOps with data governance

Panel: Building value to MLOps with data governance

Ashwin Kumar (Borneo), Balvinder Khurana (ThoughtWorks), Sathish KS (Zeotap), Srinivasa Rao Aravilli (Visa)

48 minutes11 November 2022
War stories on MLOps and data governance - learnings from Data Mesh

War stories on MLOps and data governance - learnings from Data Mesh

Madhu Podila - Data strategist at ThoughtWorks

30 minutes11 November 2022
How PayPal uses Inference Graphs to design ML systems

How PayPal uses Inference Graphs to design ML systems

Sharmili Srinivasan, Saurav Raj - ML engineers at PayPal

40 minutes11 November 2022


Microsoft Reactor


No. 9, Ground Floor, Lavelle Road

Bengaluru - 560001

Karnataka, IN


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Scribble Data builds feature stores for data science teams that are serious about putting models (ML, or even sub-ML) into production. The ability to systematically transform data is the single biggest determinant of how well these models do. Scribble Data streamlines the feature engineering proces… more


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Thoughtworks is a global technology consultancy that integrates strategy, design and engineering to drive digital innovation.

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