Twitter spaces session on data access and data management On Tuesday, 29 November, The Fifth Elephant will host a Twitter Spaces session on setting up the organizations and businesses for robust data governance. 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:
1. 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.
2. Wearing masks is optional.
Contact information: For queries about the conference, contact Hasgeek at email@example.com or call (91)7676332020.
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Back to the Future: Designing multi-model Machine learning systems using Inference Graphs
Saurav Raj (ML Engineer, PayPal)
Sharmili Srinivasan (ML Engineer, PayPal)
Machine learning inference pipelines are getting complex as they often comprise multiple models to make a prediction. These models typically follow patterns such as chaining, fanout, or ensemble in production. A streamlined model deployment mechanism that supports these patterns would reduce the effort of ML Engineers and make the ML systems more agile. In this talk, we will learn about inference graphs in ML systems and use them to design and deploy ML pipelines that require composing multiple ML models.