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:

  1. 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?
  2. 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?
  3. 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?
  4. 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?
  5. 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?
  6. 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 support@hasgeek.com or call (91)7676332020.

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Hosted by

The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more

Supported by

Primary Sponsor

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

Community Sponsor

Our technologists build bold, future-focused solutions for clients from a variety of industries. You could influence the digital strategy of a retail giant, create a new mobile application for a bank or redesign platforms using event sourcing and intelligent data pipelines. more

Venue Sponsor

Join Microsoft Reactor and learn from developers Whether you’re building your career or the next great idea, Microsoft Reactor connects you with the developers and startups that share your goals. Learn new skills, meet new peers, and find career mentorship. Virtual events are running around the clo… more
Madhusudhana Rao. Podila

Madhusudhana Rao. Podila

@madhupodila Speaker

Data Mesh: War stories on MLOps and data governance

Submitted Nov 4, 2022

Speaker

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 and processes. In this talk, we will learn about our journey in building ML use cases and the typical challenges faced in ML productionisation. The different processes that can be automated for consistency will also be explored.

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Hosted by

The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more

Supported by

Primary Sponsor

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

Community Sponsor

Our technologists build bold, future-focused solutions for clients from a variety of industries. You could influence the digital strategy of a retail giant, create a new mobile application for a bank or redesign platforms using event sourcing and intelligent data pipelines. more

Venue Sponsor

Join Microsoft Reactor and learn from developers Whether you’re building your career or the next great idea, Microsoft Reactor connects you with the developers and startups that share your goals. Learn new skills, meet new peers, and find career mentorship. Virtual events are running around the clo… more