Annual conference

Annual conference

On AI, industrial applications of ML, and MLOps

Make a submission

Accepting submissions till 30 Jun 2023, 11:59 PM

Bangalore International Centre (BIC), Bengaluru

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The Fifth Elephant 2023 edition #

There are three phases in the lifecycle of an application - research, application and aftermath of the application.

  1. Assess capabilities, determining the new frontiers for AI.
  2. Find a use for the application.
  3. Learn how to run it, monitor it and update it with time.

The three tracks at The Fifth Elephant 2023 annual conference will cover this lifecycle.

Editors #

The 2023 edition is curated by:

  1. Nischal HP, Vice President of Data Engineering and Data Science at Scoutbee. Nischal curated the MLOps conference which was held online between 23 and 27 July 2021.
  2. Sumod Mohan, Founder and CEO at AutoInfer. Sumod curated Anthill Inside 2019 edition, held in Bangalore on 23 November.

Tracks and themes #

  1. AI and research.
  2. Industrial applications of ML - covers implementation of AI in the industry, with more focus on the AI models, the issues in training, gathering data so, and so forth. ML is being used at scale in industries such as automotive, mechanical, manufacturing, agriculture, and such domains. This track focuses on the challenges in this space, as we see innovation coming out of these industries in the pursuit of using ML on a second-to-second basis.
  3. Economies of Data Science and MLOps - covers implementation of Machine Learning (ML) lifecycle at an organization and how it is helping the organization scale.

Speak at The Fifth Elephant 2023 #

If you are interested in speaking at the conference, submit your talk idea here. The editors will review your talk description and give feedback.
Guidelines for speaking, speaker honorarium policy, and travel grant policy details are published here.

Who should participate in MLOps conference? #

  1. Data/MLOps engineers who want to learn about state-of-the-art tools and techniques, especially from domains such as automobile, agri-tech and mechanical industries.
  2. Data scientists who want a deeper understanding of model deployment/governance.
  3. Architects who are building ML workflows that scale.
  4. Tech founders who are building products that require AI or ML.
  5. Product managers, who want to learn about the process of building AI/ML products.
  6. Directors, VPs and senior tech leadership who are building AI/ML teams.

Subscribe to join #

The Fifth Elephant 2023 conference will be held in-person. Attendance is open to The Fifth Elephant-Hasgeek subscribers only. Purchase a subscription to attend the conference in-person. If you have questions about participation, post a comment here.

Sponsorship #

Sponsorship slots are open for:

  1. Infrastructure (GPU, CPU and cloud providers) and developer productivity tool makers who want to evangelise their offering to developers and decision-makers.
  2. Companies seeking tech branding among AI and ML developers.
  3. Venture Capital (VC) firms and investors who want to scan the landscape of innovations and innovators in AI and who want to source leads for investment in the AI and ML space.
    If you are interested in sponsoring, email sales@hasgeek.com.

Contact information #

Join The Fifth Elephant Telegram group on https://t.me/fifthel or follow @fifthel on Twitter. For any inquiries, call The Fifth Elephant on +91-7676332020.

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

dhruvil karani

@althypothesis

Solving bias in recommender systems using negative sampling

Submitted May 12, 2023

Problem #

Recommender systems suffer a major deficiency in their feedback loops.
When a user interacts with only a few out of many items on a website, we can only assume their interest in those specific items. Hence, the feedback is biased.

Regrettably, we don’t possess data on the items the user didn’t engage with, including those that weren’t presented. Our data only pertain to the positive class, and no explicit records of the negative class regarding binary classification exist. As a result, the model performs well for a small portion of items but not for the majority.

In addition, classical algorithms like matrix factorization do not directly support cold-start settings.

Implication #

Every marketplace/social-media platform having millions of items/content pieces experience such a skew. Rarely a model can ensure consistent quality training for all items. In no time, this skew induces into the recommender system and hampers its performance.

Solution #

The above two problems can be solved by producing negative examples using negative sampling.

Outline #

In this talk, I wish to answer the following.

  1. Defining the problem with biased feedback. What happens if it is not carefully handled?
  2. How practical is this problem? What could be the potential business impact?
  3. What is negative sampling? Why should it work?
  4. What happens under the hood (accompanied by an example case study)?
  5. How to get the most out of this technique (tuning ideas)?
  6. How to measure the impact of negative sampling?
  7. Getting creative (Implementation from YouTube, Play Store, Meta, Airbnb)

references

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Make a submission

Accepting submissions till 30 Jun 2023, 11:59 PM

Bangalore International Centre (BIC), Bengaluru

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