MLOps Conference
The Fifth Elephant For members

MLOps Conference

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

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Accepting submissions till 14 Jul 2021, 11:00 PM

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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

Sajan Kedia

@sajankedia

Myntra Home Page Personalization: Optimizing Online Feature Store at Scale

Submitted Jul 9, 2021

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 get user & widget features in real-time.
Architecture design explains all the components in a detailed fashion. Components discussed: App service, ML Platform, Feature Store Lookup, Aggregation, Encoder, Model Predict, Ranking. There are four broad categories of features used: user embedding, user activity counts, widget embedding, widget business metrics. One of the major tech challenges is to fetch all the features in real-time with very low latency. In many cases feature lookup is more expensive than the actual Model prediction. To reduce the feature lookup time, we’ve done five types of Optimization, which is giving us ~ 10X improvement. I’ll be explaining why we choose Aerospike as the feature store, will share a detailed comparison with another alternative DB.
Will be talking about the end-to-end system design comprising different layers like Sources, Ingestion, Processing, Storage, MLP, Serving, Client. In the end, we’ll demonstrate the benchmark and load test of the personalization service for a very huge scale.

Agenda:

  • Objective
  • Tech Challenges
  • Features
  • System Design
  • Feature Store Lookup
  • Architecture Design
  • Why Aerospike
  • Optimizations
  • Benchmarking

Key takeaways:

  • Ideal Feature Store Choice for real-time feature lookup from Millions of user’s feature data.
  • Different Optimizations to reduce the Feature lookup latency in aerospike.
  • Why Aerospike?
  • How to productionize ML Models at the scale.
  • How ML Platform talks to different components & layers in real-time for each API call.
  • Learning about the end-to-end system design, starting from Source, Ingestion, Processing, Storage, MLP, Serving.
  • Benchmarking & Load test for the entire ML service.

Audience:
Anyone looking for Productionization of ML Models, Feature Pipelines, Feature Store working at scale.
Ideal for the entire ML community.

Audience Level: Intermediate

Slides: https://drive.google.com/file/d/1eTWv09SoMB5573rG_U-vz1VXw3k5LR5k/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