MLOps Conference
On DataOps, productionizing ML models, and running experiments at scale.
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
Sajan Kedia, Data Scientist at Myntra
41 minutes27 July 2021
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
Schaun Wheeler, Co-founder at Aampe
1 hour24 July 2021
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
Venkata Pingali, Co-founder and CEO at Scribble Data
19 minutes24 July 2021
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?
Lavanya Tekumala, Founder at MachineLearningInterview.com
37 minutes24 July 2021
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
Haridas N, Architect at Imaginea Technologies Inc
31 minutes24 July 2021
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
Swaroop Ch, Staff Engineer, Machine Learning Platform at Doordash
24 minutes23 July 2021
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
Milecia McGregor, Developer Advocate at Iterative, DVC
50 minutes23 July 2021
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
Vishal Gupta, Machine Learning Engineer at Freshworks
49 minutes23 July 2021
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
Sandilya Konduri, Group Product Manager at Flipkart; Bharadwaaj Rajan, Product Manager at Flipkart
36 minutes23 July 2021
Hybrid access (members only)
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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…
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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:
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