MLOps Conference
On DataOps, productionizing ML models, and running experiments at scale.
Friday 23 July 2021
12:00–12:15
Introduction to MLOps conference
Nischal HP, Editor of MLOps 2021 conference; Director of Data at Scoutbee
12:15–12:25
Theme - Machine Learning Operations
Nirant Kasliwal, Emcee
12:25–13:00
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
13:00–13:25
End-to-end serverless transformers on AWS Lambda for NLP
Pratik Bhavsar, Founding engineer at Enterpret
13:25–14:05
Jupyter to Jupiter : scaling multi-tenant ML pipelines
Vishal Gupta, Machine Learning Engineer at Freshworks
14:05–14:35
Speaker connect session: with Pratik Bhavsar and Vishal Gupta
14:35–14:50
Break
14:50–15:15
MLOps for startups
Nirant Kasliwal, ex-Machine Learning/Data Science Lead at Verloop. Introducer: Paul Meinhausen, founder at Aampe
15:15–15:45
Birds of Feather (BOF) session - Running MLOps in large and small organizations
Moderated by Paul Meinhausen, founder at Aampe. Participants: Nirant Kasliwal, Vishal Gupta
15:45–16:00
Flash talks
By participants
16:00–16:50
Tuning hyperparameters with DVC experiments
Milecia McGregor, Developer Advocate at Iterative, DVC
16:50–17:40
Model Health Assurance at scale at LinkedIn
Rajeev Kumar, Staff Software Engineer for AI Platform at LinkedIn
17:40–18:05
Maintaining Machine Learning model accuracy through monitoring
Swaroop Ch, Staff Engineer, Machine Learning Platform at Doordash
18:05–18:15
Summary for the day's sessions; key takeaways
Saturday 24 July 2021
12:00–12:10
Theme: Machine Learning Operations
Amit Kapoor, Emcee
12:10–12:30
Automatic rollbacks for MLOps deployments in Kubernetes
Neha Gupta, Shubham Jain - Founders at HybridK8s
12:30–13:00
How to open source your ML service
Haridas N, Architect at Imaginea Technologies Inc
13:00–13:10
Break
13:10–13:20
Theme: Privacy, security and ethics in Machine Learning
Amit Kapoor, Emcee
13:20–13:40
Privacy attacks in Machine Learning systems - discover, detect and defend
Upendra Singh, Machine Learning Architect at Twilio
13:40–14:15
How do we build unbiased ML workflows and achieve fairness in Machine Learning?
Lavanya Tekumala, Founder at MachineLearningInterview.com
14:15–14:25
Break
14:25–14:50
Fighting fraudsters in email communication at Twilio using Machine Learning
Sachin Nagargoje, Staff Data Scientist at Twilio
14:50–15:20
Birds of Feather session: Doing privacy, security and ethics in Machine Learning
Upendra Singh, Sachin Nagargoje
15:20–15:30
Theme: Economies of Machine Learning
Amit Kapoor, Emcee
15:30–15:45
Sponsored talk: Scribble Enrich - second generation Feature Engineering platform
Venkata Pingali, Co-founder and CEO at Scribble Data
15:45–16:15
Rethinking linguistic diversity and inclusion in the context of technology
Monojit Choudhury, Principal Researcher at Microsoft Research Lab, India
16:15–17:00
Story-telling as a method for building production-ready Machine Learning systems
Schaun Wheeler, Co-founder at Aampe
17:00–17:10
Summary for the day's sessions; key takeaways
Tuesday 27 July 2021
14:00–14:10
Theme: Feature Store in ML
Amit Kapoor, Emcee
14:10–14:40
Past and future of feature stores
Venkata Pingali, Co-founder and CEO at Scribble Data
14:40–15:15
Myntra home page personalization: optimizing online Feature Store at scale
Sajan Kedia, Data Scientist at Myntra
15:15–15:55
Managed Feature Store: Improving data reusability & Providing a means for low latency real-time prediction at Udaan
Sai Sharan Tangeda, Mohit Kumar
15:55–16:35
Birds of Feather (BOF) session - Feature Stores - adaptations, use cases and ROI
Sai Sharan Tangeda, Sajan Kedia and Venkata Pingali
16:35–16:55
Flash talks by participants
16:55–17:05
Summary for the day's sessions; key takeaways.
17:05–17:10
Oct-Nov edition of MLOps
Hybrid access (members only)
Hosted by
Jump starting better data engineering and AI futures
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