MLOps Conference

MLOps Conference

On DataOps, productionizing ML models, and running experiments at scale.



Machine Learning (ML) is at the helm of products. As products evolve with time, so is the necessity for ML to evolve. In 2010s, we saw DevOps culture take the forefront for engineering teams. 2020s will be all about MLOps.

MLOps stands for Machine Learning Operations. MLOps mainly focuses on workflows, thought processes and tools that are used in creating ML models, and their evolution over time. The workflows for ML at organizations are different as the problem space, maturity of teams and experience with ML tools are widely different.

MLOps relies on DataOps. DataOps is about Data operations, and helps define data and SLOs for data - how they are stored, managed and mutate over time - thereby providing the foundations for sound ML. The success and failure of ML models depends heavily on DataOps, where data is well-managed and brought into the system in a well thought out manner. ML and data processes have to evolve to provide insights into the reasons as to why certain models are not behaving as before.

Productionizing ML models is a challenge, but so is running experiments at scale. MLOps caters not only to scaling ML models in production, but also helps in providing guidelines and thought processes to support rapid prototyping and research for ML teams.

MLOps Conference 2021 edition

The 2021 edition is curated by Nischal HP, Director of Data at Scoutbee.

The conference covers the following themes:

  1. Machine Learning Operations
  2. Machine Learning in Production
  3. Privacy and Security in Machine Learning
  4. Tooling and frameworks in Machine Learning
  5. Economies of Machine Learning

Speakers from Doordash, Twilio, Scribble Data, Microsoft Research Labs India, Freshworks, Aampe, Myntra, Farfetch and other organizations will share their experiences and insights on the above topics.


Who should participate in MLOps conference?

  1. Data/MLOps engineers who want to learn about state-of-the-art tools and techniques.
  2. Data scientists who want a deeper understanding of model deployment/governance.
  3. Architects who are building ML workflows that scale.
  4. Tech founders who are building products that require ML or building developer productivity products for ML.
  5. Product managers, who are seeking to learn about the process of building ML products.
  6. Directors, VPs and senior tech leadership who are building ML teams.

Contact information: Join The Fifth Elephant Telegram group on or follow @fifthel on Twitter. For inquiries, contact The Fifth Elephant on or call 7676332020.

Featured submissions

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  • upendra singh

    Privacy Attacks in Machine Learning Systems - Discover, Detect and Defend

    My name is Upendra Singh. I work at Twilio as an Architect. As a part of this talk proposal I would like to shed some light on the new kind of attacks machine learning systems are facing nowadays - Privacy Attacks. During the talk we will explain and demonstrate how to discover, detect and defend Privacy related vulnerabilities in our machine learning models. Will also explain why it is so critic… more

    17 Apr 2021

    Supported by

  • haridas n

    Video thumbnail

    Process of Open Source your ML Service

    Pic2Card is an Opensource ML service that helps to create AdaptiveCards from an Image. We have recently contributed this service to AdaptiveCards, an Opensource card authoring framework from Microsoft. more

    17 Apr 2021

  • Venkata Pingali

    Venkata Pingali

    Past and Future of Feature Stores

    Audience Level: Intermediate Nature: Conceptual more

    11 May 2021

  • Monojit Choudhury

    Massively Multilingual Neural Language Models: The new hope and fear for low resource languages

    Natural Language processing is undergoing a paradigm shift right now; the models today are ever more powerful, capable and accurate. Thus, it looks like we are close to achieving the holy grail of AI. However, these assertions are true only for a handful of the world’s languages which have enough language data to train powerful and large models. What about the rest of the languages? In this talk,… more

    11 Jun 2021

  • Milecia McGregor

    Tuning Hyperparameters with DVC Experiments

    When you start exploring multiple model architectures with different hyperparameter values, you need a way to quickly iterate. There are a lot of ways to handle this, but all of them require time and you might not be able to go back to a particular point to resume or restart training. more

    14 Jun 2021

  • swaroopch

    Maintaining Machine Learning Model Accuracy Through Monitoring

    Machine Learning models begin to lose accuracy as soon as they are put into production. At DoorDash, we implemented a robust monitoring system to diagnose this issue and maintain the accuracy of our forecasts. more

    15 Jun 2021

  • Schaun Wheeler

    Story-telling as a method for building production-ready machine-learning systems

    This presentation is about the essential but overlooked role storytelling plays in machine learning productionization. Although often assumed to be at best a tangential concern, machine learning systems only become successful through successfully building trust in the model, the data, and the system in which the model and data operate. That trust-building happens through storytelling. Most often,… more

    16 Jun 2021

  • sachin nagargoje

    Fighting Fraudsters in Email Communication at Twilio using Machine Learning

    My name is Sachin Nagargoje. I work at Twilio as a Staff Data Scientist. As a part of this talk proposal I would like to shed some light on the kind of attacks we are facing at Twilio nowadays and how we are tackling it via different innovative ways and Machine Learning techniques. I want to showcase what are the challenges we face, and how we do and what we do to catch such unwanted communicatio… more

    02 Jun 2021

    Supported by

  • lavanya TS

    Fairness in ML: How do we build unbiased ML workflows?

    Biases often arise in automated workflows based on MachineLearning models due to erroneous assumptions made in the learning process. Examples of such biases involve societal biases such as gender bias, racial bias, age bias and so on. more

    29 Jun 2021

    Supported by

  • Nirant K

    Nirant K

    ML Ops for Startups

    Context: Early Startup While a plethora of MLOps work has been done at large data and model serving scale, this is an attempt to introduce you to different ops problems, tools and how to pick among them. In most cases, I’ll share an example to anchor the idea set further. more

    11 Jul 2021

  • Sandilya Konduri

    Leveraging ML algorithms to deliver faster in an extremely chaotic environment

    Ecommerce in our country has come quite some distance and along with it has been successful in reshaping customer expectations and his/her perception of hygiene & delight. Speedy delivery now has become a base expectation of today’s customer. Add a certain set of categories , the need for quicker SLA just escalates higher. more

    14 Jul 2021

  • Sajan Kedia

    Myntra Home Page Personalization: Optimizing Online Feature Store at Scale

    This talk is mainly about productionizing ML Model and Optimizing Online Feature Store at the scale of India’s Biggest Fashion E-Commerce. Myntra Home Page consists of widgets(targeted cards). Currently, their ranking is the same for all the users based on the business metrics(CTR, Revenue). To truly personalize the home page, we’ve deployed the ranking ML Model which talks to feature stores to g… more

    09 Jul 2021


See all
Managed Feature Store: Improving data reusability & Providing a means for low latency real-time prediction at Udaan

Managed Feature Store: Improving data reusability & Providing a means for low latency real-time prediction at Udaan

Sai Sharan Tangeda, Mohit Kumar

38 minutes27 July 2021
Myntra home page personalization: optimizing online Feature Store at scale

Myntra home page personalization: optimizing online Feature Store at scale

Sajan Kedia, Data Scientist at Myntra

41 minutes27 July 2021
Past and future of feature stores

Past and future of feature stores

Venkata Pingali, Co-founder and CEO at Scribble Data

33 minutes27 July 2021
Story-telling as a method for building production-ready Machine Learning systems

Story-telling as a method for building production-ready Machine Learning systems

Schaun Wheeler, Co-founder at Aampe

1 hour24 July 2021
Rethinking linguistic diversity and inclusion in the context of technology

Rethinking linguistic diversity and inclusion in the context of technology

Monojit Choudhury, Principal Researcher at Microsoft Research Lab, India

35 minutes24 July 2021

Hosted by

All about data science and machine learning

Supported by

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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