The Fifth Elephant 2023 Monsoon edition schedule announcement; workshop on AI readiness for organizations Woohoo! After sifting through 60-odd talks for The Fifth Elephant monsoon edition, we has a schedule **Talks from Myntra, Samsung Ads, LinkedIn, DoorDash, Aa… more
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:
- How do you add value to your MLOps processes?
- What are the key performance indicators?
- How do you quantify the impact of MLOps in your organisation?
- What is the value addition to the business?
- How do you measure the Return of Investment (RoI)?
- What is the thriving work culture in your team/company?
- How to design cost-effective pipelines?
- Are you investing in making the models leaner - distillation, quantization, pruning etc?
- What is the comparison of your hardware and performance costs?
- What are the common mistakes made and lessons learnt in implementing MLOps?
- How did you resolve and recover from the mistakes made?
- Are you meeting SLA requirements and doing inference cost analysis?
- What are some of your favourite tools for implementing MLOps?
- What are the key factors in deciding on a specific framework?
- When do you choose to build your own tool?
- As an experienced MLOps practitioner, what advice do you provide?
- What advice would you give for beginners who are starting on MLOps?
- How do you scale your existing MLOps infrastructure?
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:
- Participants attending in person must keep their vaccination certificate handy. The venue will ask you to show your vaccination certificate as proof of being fully vaccinated.
- Wearing masks is optional.
Contact information: For queries about the conference, contact Hasgeek at firstname.lastname@example.org or call (91)7676332020.
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Do the Right Thing: Fast Development of ML applications using Sub-ML Feature Stores
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.