Nov 2022
7 Mon
8 Tue
9 Wed
10 Thu
11 Fri 09:30 AM – 04:00 PM IST
12 Sat
13 Sun
Nov 2022
7 Mon
8 Tue
9 Wed
10 Thu
11 Fri 09:30 AM – 04:00 PM IST
12 Sat
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MLOps 2022 conference will bring together Machine Learning (ML), data engineering and SRE and AI practitioners to better understand the art of implementing MLOps in organizations.
MLOps is based on similar principles of DevOps, which talks about Continuous Delivery (CD) of ML models in production. We have seen significant traction for MLOps in recent years. Companies have been built around organizing and optimizing MLOps. We will focus on the tools, frameworks, data governance, decision-making process and approach based on real-world experience that have led to success and war stories.
The topics that we will focus in this conference include:
In-person conference: MLOps 2022 will be held on Friday, 11 November, at Microsoft Reactor, Lavelle Road, Bangalore. This is an in-person conference. Register to participate for the conference.
Code of Conduct: Hasgeek’s Code of Conduct applies to the MLOps 2022 conference - participants, speakers and sponsors.
COVID protocols and masking policy: In keeping with COVID protocols, the following is applicable to all participants:
Contact information: For queries about the conference, contact Hasgeek at support@hasgeek.com or call (91)7676332020.
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Submitted Oct 27, 2022
Venkata Pingali, Co-Founder & CEO, Scribble Data
Widespread adoption of machine learning (ML) in industry is still a challenge today due to resource constraints and RoI questions. Production ML approaches today require high skill, rely on large volumes of data, and have long delivery timelines. In this talk, we argue for Sub-ML - a class of ML simpler than traditional ML approaches, often designed to be used in decision support systems, and delivered under tight constraints. Sub-ML, also called as ML-at-reasonable-scale (MLRS) and Analytical ML, covers upto 80% of the ML usecases in an enterprise. Characterized by their speed in realizing business value and support for diverse use cases, Sub-ML applications still require guarantees of correctness, transparency, and auditability in the data transformation process. We draw on our experience in the fin-tech, ed-tech and e-commerce domains to lay out design choices for feature stores to enable Sub-ML, tradeoffs we made including constraining the problem space, bundling capabilities for fast development, and incorporating a data consumption layer.
https://www.slideshare.net/pingali/fast-subml-usecase-developmentpdf
Nov 2022
7 Mon
8 Tue
9 Wed
10 Thu
11 Fri 09:30 AM – 04:00 PM IST
12 Sat
13 Sun
Hosted by
Supported by
Sponsor
Promoted
Community Sponsor
Venue Sponsor
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