Videos
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
Maintaining Machine Learning model accuracy through monitoring

Maintaining Machine Learning model accuracy through monitoring

Swaroop Ch, Staff Engineer, Machine Learning Platform at Doordash

24 minutes29 July 2021
Jupyter to Jupiter : scaling multi-tenant ML pipelines

Jupyter to Jupiter : scaling multi-tenant ML pipelines

Vishal Gupta, Machine Learning Engineer at Freshworks

49 minutes29 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 hour29 July 2021
Privacy attacks in Machine Learning systems - discover, detect and defend

Privacy attacks in Machine Learning systems - discover, detect and defend

Upendra Singh, Machine Learning Architect at Twilio

27 minutes29 July 2021
Fighting fraudsters in email communication at Twilio using Machine Learning

Fighting fraudsters in email communication at Twilio using Machine Learning

Sachin Nagargoje, Staff Data Scientist at Twilio

20 minutes29 July 2021
MLOps for startups

MLOps for startups

Nirant Kasliwal, ex-Machine Learning/Data Science Lead at Verloop. Introducer: Paul Meinhausen, founder at Aampe

37 minutes29 July 2021
End-to-end serverless transformers on AWS Lambda for NLP

End-to-end serverless transformers on AWS Lambda for NLP

Pratik Bhavsar, Founding engineer at Enterpret

30 minutes29 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 minutes29 July 2021
Model Health Assurance at scale at LinkedIn

Model Health Assurance at scale at LinkedIn

Rajeev Kumar, Staff Software Engineer for AI Platform at LinkedIn

1 hour29 July 2021
How to open source your ML service

How to open source your ML service

Haridas N, Architect at Imaginea Technologies Inc

31 minutes29 July 2021
Leveraging ML algorithms to deliver faster in a chaotic environment - Flipkart's case study

Leveraging ML algorithms to deliver faster in a chaotic environment - Flipkart's case study

Sandilya Konduri, Group Product Manager at Flipkart; Bharadwaaj Rajan, Product Manager at Flipkart

36 minutes29 July 2021
Sponsored talk: Scribble Enrich - second generation Feature Engineering platform

Sponsored talk: Scribble Enrich - second generation Feature Engineering platform

Venkata Pingali, Co-founder and CEO at Scribble Data

19 minutes29 July 2021
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 minutes29 July 2021
Tuning hyperparameters with DVC experiments

Tuning hyperparameters with DVC experiments

Milecia McGregor, Developer Advocate at Iterative, DVC

50 minutes29 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 minutes29 July 2021
Past and future of feature stores

Past and future of feature stores

Venkata Pingali, Co-founder and CEO at Scribble Data

33 minutes29 July 2021
Automatic rollbacks for MLOps deployments in Kubernetes

Automatic rollbacks for MLOps deployments in Kubernetes

Neha Gupta, Shubham Jain - Founders at HybridK8s

19 minutes29 July 2021
How do we build unbiased ML workflows and achieve fairness in Machine Learning?

How do we build unbiased ML workflows and achieve fairness in Machine Learning?

Lavanya Tekumala, Founder at MachineLearningInterview.com

37 minutes29 July 2021
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