Videos
MLOps Conference

MLOps Conference

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

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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
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 minutes24 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 minutes24 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 minutes24 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 minutes24 July 2021
How to open source your ML service

How to open source your ML service

Haridas N, Architect at Imaginea Technologies Inc

31 minutes24 July 2021
Automatic rollbacks for MLOps deployments in Kubernetes

Automatic rollbacks for MLOps deployments in Kubernetes

Neha Gupta, Shubham Jain - Founders at HybridK8s

19 minutes24 July 2021
Maintaining Machine Learning model accuracy through monitoring

Maintaining Machine Learning model accuracy through monitoring

Swaroop Ch, Staff Engineer, Machine Learning Platform at Doordash

24 minutes23 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 hour23 July 2021
Tuning hyperparameters with DVC experiments

Tuning hyperparameters with DVC experiments

Milecia McGregor, Developer Advocate at Iterative, DVC

50 minutes23 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 minutes23 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 minutes23 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 minutes23 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 minutes23 July 2021
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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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