Accepting submissions
Not accepting submissions
Speaker selection process
The process for curating the conference schedule is as follows:
- The Fifth Elephant team reached out to individuals and companies, inviting them to speak at the conference.
- Companies and individuals who agreed to speak submitted their abstracts.
- Slides were reviewed before the conference.
- Talks which did not fit the theme have been moved to The Fifth Elephant conference.
- Two talks and two panel discussions have been sponsored. These have been mentioned as sponsored in the conference schedule.
Nischal HP and Shakthi Kannan have curated the conference.
MLOps 2022 conference reportContext This is a report of the MLOps 2022 conference, held on 11 November, at the Microsoft Reactor, Bangalore. more
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Venkata Pingali Speaker Do the Right Thing: Fast Development of ML applications using Sub-ML Feature StoresSpeaker Venkata Pingali, Co-Founder & CEO, Scribble Data more
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Sharmili Srinivasan Speaker SR
Saurav Raj Speaker Back to the Future: Designing multi-model Machine learning systems using Inference GraphsSpeakers Saurav Raj (ML Engineer, PayPal) Sharmili Srinivasan (ML Engineer, PayPal) more
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Sai Sharan Tangeda Speaker Come and see ML at scale: Unified ML Platform at UdaanSpeakers Dr Mohit Kumar (VP & Head of Data Science, Data Platform and Product Analytics, Udaan) more
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RM
Ravi Kumar Meduri Speaker 2022: An Operationalisation Odyssey of AI - From Labs to ProductionSpeaker Ravi Kumar Meduri (Executive Vice President - IP and Digital Solutions) more
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KS
Kannan S Building Computer Vision models using Azure AutoMLSpeaker Vivek Dani (Data and Applied Scientist) Abstract more
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Madhusudhana Rao. Podila Speaker Data Mesh: War stories on MLOps and data governanceSpeaker Madhu Podila (Data Strategist) Abstract The operation, process workflow and automation for ML models are challenges for any organisation. The transparency and visibility of these models across ecosystems are important to provide explanations on informed decisions. In order to conform to regulations and meet stakeholder expectations, it is also important to use the right operating models a… more
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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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