The Fifth Elephant 2023 Monsoon Edition event recap is now up here . The event was attended by 192 participants, of which one-fourth were women. The Fifth Elephant videos are available to watch here
Event highlights:
Editors
The 2023 Monsoon edition is curated by:
- 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.
- Sumod Mohan, Founder and CEO at AutoInfer. Sumod curated Anthill Inside 2019 edition, held in Bangalore on 23 November.
Tracks and themes
- AI and Research - covers research, findings, and solutions for challenges on building models in various areas such as fraud detection, forecasting, and analytics. This track delves into the latest methodologies for handling challenges such as large-scale data processing, distributed computing, and optimizing model performance.
- 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.
- AI and Product - covers strategies for building AI products to scale and mitigating challenges. This track provides insights on incorporating AI tools and forecasting techniques to improve model training, developing a working model architecture, and using data in the business context.
There are three phases in the lifecycle of an application - research, application and aftermath of the application.
- Assess capabilities, determining the new frontiers for AI.
- Find a use for the application.
- Learn how to run it, monitor it and update it with time.
The three tracks at the 2023 Monsoon edition of The Fifth Elephant will cover this lifecycle.
Members-only conference
The Fifth Elephant 2023 Monsoon edition will be held in-person. Attendance is open to The Fifth Elephant members only. Pick a membership to attend the in-person conference. If you have questions about participation, post a comment here.
Who will benefit from participating in The Fifth Elephant community:
- 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.
- Data scientists who want a deeper understanding of model deployment/governance.
- Architects who are building ML workflows that scale.
- Tech founders who are building products that require AI or ML.
- Product managers, who want to learn about the process of building AI/ML products.
- Directors, VPs and senior tech leadership who are building AI/ML teams.
Sponsorship
Sponsorship slots are open for:
- Infrastructure (GPU, CPU and cloud providers) and developer productivity tool makers who want to evangelise their offering to developers and decision-makers.
- Companies seeking tech branding among AI and ML developers.
- 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.
Contact information
Join the @fifthel Telegram group or follow @fifthel on Twitter. For any inquiries, call Hasgeek at +91 7676 33 2020.
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AT
Aditi Tuli
@aditituli
Algorithm Friendly Design: Aligning product design with algorithmic requirements.
Submitted Jun 30, 2023
In the ever-changing landscape of digital products, algorithms play a vital role in providing personalized experiences for users. This talk will delve into the intersection of product and algorithmic design, addressing the need to create products that optimize algorithmic capabilities. For example, at Glance, a lock screen experience, we have 200m+ daily active users and need to design our user experience with our recommendation system in mind.
Product design from an algorithmic perspective:
The term algorithm friendly product design was coined by Eugene Wei and addresses the unique challenge faced when building a product that uses an algorithm: how do you allow your algorithm to see what it needs to see? This section of the talk will cover the challenges of creating training data, with a particular focus on implicit data collection – uncovering user preferences without explicitly asking the user what they like. It will also discuss ways to improve algorithmic feedback loops through intentional product design, such as isolated feedback loops, creating user friction, and separating exploration and exploitation.
Algorithmic design from an HCI perspective:
Given the mutual dependance between algorithms and UX design, it may not be beneficial to evaluate the performance of an algorithm and the performance of UX design in isolation from each other. This section will discuss how subjective system aspects like user perception mediates the relationship between the objective system (the algorithm itself) and objective measures of user experience. It will also touch on the ways in which user sentiment towards a product’s algorithm can affect user experience as a whole. This will build to the argument that evaluation of performance needs to be more holistic and consider the user experience and the algorithm together, moving beyond traditional measures of algorithmic performance like accuracy.
Best practices for algorithm friendly design:
Drawing on the above two sections and on examples from industry, this section of the talk will present some best practices for algorithm friendly design and sum up the challenges and opportunities involved in designing products that work in conjunction with algorithms, rather than parallel to them. It will make the case for moving away from the traditional siloed structure to a deeper integration between teams that design user experiences and teams that develop algorithms. This will be one concrete takeaway from the talk; it is important to align product design with algorithmic requirements through effective collaboration between user experience and data science teams, right from the start of the design process.
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