Submissions
MLOps Conference

MLOps Conference

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

ML workflows and processes are critical for enabling rapid prototyping and deployment of ML/AI models in any organization. This conference is a platform to present and discuss such workflows and patterns that drive data-driven organizations to operate at scale. expand

ML workflows and processes are critical for enabling rapid prototyping and deployment of ML/AI models in any organization. This conference is a platform to present and discuss such workflows and patterns that drive data-driven organizations to operate at scale.

We are accepting experiential talks and written content on the following topics:

  1. ML development workflows.
  2. ML deployment frameworks.
  3. Data lineage.
  4. Model lineage.
  5. Model ethics/bias testing.
  6. A/B testing frameworks.
  7. Model governance.
  8. Explainability/interpretability of models in run-time.
  9. Impact of change in MLOps mindset in product organizations.
  10. DataOps workflows.
  11. DataOps frameworks.
  12. Alerting, monitoring and managing models in production.
  13. Growing and managing data teams.
  14. MLOps in research.
  15. Deployment and infrastructure for machine learning.
  16. ROI (Return on Investment) for MLOps.

Who should speak?

  1. MLOps engineers who build and maintain ML workflows and deploy ML models.
  2. Data engineers building production scale data pipelines, feature stores, model dashboards, and model maintenance.
  3. Tech leaders/engineers/scientists/product managers of companies who have built tools and products for ML productivity.
  4. Tech leaders/engineers/scientists/product managers of companies who have built tools, products, processes for Data Ops to support ML.
  5. Tech Leaders/engineers/scientists/product managers who have experience with products that failed to make a mark in the market due to ML failures.
  6. Investors who are investing in the space of ML productivity tools, frameworks and landscape.
  7. Privacy/ethics stakeholders involved in model governance and testing for ethics/bias.

Content can be submitted in the form of:

  • 15 minute talks
  • 30 minute talks
  • 1,000 word written articles

All content will be peer-reviewed by practitioners from industry.

Make a submission

Submissions are closed for this project

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
  • 24 comments
  • Confirmed & scheduled
  • 17 Apr 2021

haridas n

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
  • 12 comments
  • Confirmed & scheduled
  • 17 Apr 2021
Venkata Pingali

Venkata Pingali

Past and Future of Feature Stores

Audience Level: Intermediate Nature: Conceptual https://drive.google.com/file/d/1WpKUXeC0i93f72vC8yXtUR6unPEaB4ma/view?usp=sharing more
  • 10 comments
  • Confirmed & scheduled
  • 11 May 2021
Venkata Pingali

Venkata Pingali

Scribble Enrich - 2nd Generation Feature Engineering Platform

Scribble Enrich - 2nd Generation Feature Engineering Platform more
  • 3 comments
  • Confirmed & scheduled
  • 11 May 2021

Vishal Gupta

Jupyter to Jupiter : Scaling multi-tenant ML Pipelines

A brief talk summarising the journey of an ML feature from a Jupyter Notebook to production. At Freshworks, given the diverse pool of customers using our products, each feature has dedicated models for each account, churning out millions of predictions every hour. This talk shall encompass the different tools and measures we’ve used to scale our ML products. Additionally, I’ll also be touching up… more
  • 10 comments
  • Confirmed & scheduled
  • 22 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
  • 3 comments
  • Confirmed & scheduled
  • 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
  • 3 comments
  • Confirmed & scheduled
  • 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
  • 5 comments
  • Confirmed & scheduled
  • 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
  • 2 comments
  • Confirmed & scheduled
  • 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
  • 10 comments
  • Confirmed & scheduled
  • 02 Jun 2021

Pratik Bhavsar

End2End Serverless Transformers On AWS Lambda For NLP

Transformers are everywhere! But how to serve them? How do you leverage serverless to get scalability without any worries? Isn’t serverless used for light applications?. How to get the best latencies with your serverless? I will be sharing answers to these questions in my talk. more
  • 3 comments
  • Confirmed & scheduled
  • 27 Jun 2021

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
  • 3 comments
  • Confirmed & scheduled
  • 29 Jun 2021

Neha Gupta

Automatic rollbacks for MLOps deployments in Kubernetes

While there are different tooling to automate deployments of ML models most of them require manually written rules for verifying deployments in production. more
  • 7 comments
  • Confirmed & scheduled
  • 18 Jun 2021

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
  • 1 comments
  • Confirmed & scheduled
  • 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
  • 0 comments
  • Confirmed & scheduled
  • 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
  • 3 comments
  • Confirmed & scheduled
  • 09 Jul 2021

Rajeev Kumar

Model Health Assurance at scale at LinkedIn

At LinkedIn, AI models drive numerous use-cases - across ranking (eg: Feed items, jobs relevance), and classification (wg: content quality, account protection) use-cases - with hundreds of experiments performed every week. At this scale, it is also important to standardize and platformize how the said monitoring is also done so that we don’t reinvest on the same things, we can effect change acros… more
  • 0 comments
  • Confirmed & scheduled
  • 14 Jul 2021
Sai Sharan Tangeda

Sai Sharan Tangeda

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

A brief talk on Managed Feature Store built on top of Open Source Feast. We will start with a brief walkthrough of the open source Feast feature store including the architecture and core capabilities. We would call out some of the challenges/limitations of the open source Feast feature store. We would then describe some of the enhancements which enables us to have a more robust, secure and scalab… more
  • 3 comments
  • Confirmed & scheduled
  • 11 Jul 2021

Mario Rozario

ML Model Scaling with Teradata Vantage

My name is Mario Rozario and I work at Teradata. As psrt of this talk I would be briefly covering our latest product Vantage and some of its inherent strengths. One of the biggest benefits of Vantage is the ability to now scale Machine Learning workloads based on our parallel technology. Now with Vantage, users will also be able to scale ML workloads that they build elsewhere and scale them here.… more
  • 8 comments
  • Rejected
  • 16 Apr 2021
Make a submission

Submissions are closed for this project

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The Fifth Elephant - known as one the best #datascience and #machinelearning conference in Asia - is transitioning into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more

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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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Privacy Mode is a forum for discussions on privacy, data security and compliance. Participate and collaborate with India’s pioneering privacy-tech community. Industry surveys: In 2020, Privacy Mode executed two surveys: more