MLOps Conference

MLOps Conference

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

Make a submission

Accepting submissions till 14 Jul 2021, 11:00 PM

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.

Schedule: https://hasgeek.com/fifthelephant/mlops-conference/schedule

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 https://t.me/fifthel or follow @fifthel on Twitter. For inquiries, contact The Fifth Elephant on fifthelephant.editorial@hasgeek.com or call 7676332020.

Hosted by

The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more

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

Promoted

Deep dives into privacy and security, and understanding needs of the Indian tech ecosystem through guides, research, collaboration, events and conferences. Sponsors: Privacy Mode’s programmes are sponsored by: more
Sai Sharan Tangeda

Sai Sharan Tangeda

@sai-sharan-t

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

Submitted Jul 11, 2021

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 scalable deployment by using a) managed resources on Cloud platforms for eg, Kafka vs Event Hub (Azure), open source Spark vs Databricks; b) Integration of RBAC & Table Level Access Control to maintain controlled usage c) Scalable batch ingestion by using Spark instead of Pandas & addition of new capabilities to increase data reusability.

Speakers:
Dr Mohit Kumar (Head - Data Science, Product Analytics and Data Platform)
Sai Sharan Tangeda (Data Scientist)
Time: 30 mins

Agenda

  1. Introduction
    1. Introduction
    2. Motivation for maintaining a Managed Feature Store
  2. Feast (Open Source): Constructs, Core Capabilities & Limitation
    1. Constructs & Architecture of Feast
    2. Point In Time Join Capabilities with Batch Retrieval
    3. Batch Ingestion into Historical Store & Scale Limitations
    4. Streaming Capabilities with Apache Kafka & Redis
    5. Reliability issues with self deployed resources like Kafka, Redis, PostgreSQL
  3. Managed Feature Store as a fork of Feast
    1. Overview of Core Architecture
    2. Integration of Azure Eventhubs as a replacement for Apache Kafka
    3. Introducing Databricks as Spark Backend
    4. Ensuring Scalability for large data sizes via Spark
    5. RBAC & Table Level Access Control for controlled usage
    6. End-to-End flow for real-time model serving
  4. Closing Arguments
    1. Increase in Productivity with ready-to-use Features

Link to slides: https://drive.google.com/file/d/1ocJNDbEUxXVJqyBVD35k-Vvr1y8hjN5k/view?usp=sharing

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Make a submission

Accepting submissions till 14 Jul 2021, 11:00 PM

Hosted by

The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more

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

Promoted

Deep dives into privacy and security, and understanding needs of the Indian tech ecosystem through guides, research, collaboration, events and conferences. Sponsors: Privacy Mode’s programmes are sponsored by: more