AI & Research,And Industrial Tracks - all videos, Also inviting registrations for Signal In Bangalore This update is for participants only
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.
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.
- 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.
The Fifth Elephant 2023 Monsoon edition will be held in-person. Attendance is open to The Fifth Elephant members only. Purchase a membership to attend the conference in-person. If you have questions about participation, post a comment here.
- 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 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.
Space Models: Optimizing Store Space Allocation at Target
In the retail industry, Category Managers and Buyers often rely on their experience and instincts when planning the allocation of space in a store. However, these traditional approaches may not be accurate or adaptable to changing market dynamics. Furthermore, numerous factors influencing space planning decisions may go unnoticed by decision makers.
To address these challenges and enable data-driven decision making, the Space and Presentation Data Science team has developed the Space Models. These models incorporate historical trends and consider various factors that impact space planning decisions.
The Space Models provide optimization recommendations for space allocation across 1800 full-format stores and 200 small-format stores, and it covers about 40 divisions and thousands of categories. This enables us to achieve gradual and continuous improvements in sales.
The Space Models have been developed at two levels of granularity:
- Enterprise Models are built at the division level, focusing on specific categories such as Beauty.
- Category Models focus on individual categories within the divisions, such as Haircare and Skincare.
The process of building Space Models involves the following steps:
- Building regression curves using Multivariate Regression techniques that predict sales and gross margin based on the allocated space.
- Determining the optimal space allocation on these curves to maximize sales and gross margin. The optimization involves Mixed Integer Linear Programming to solve a 0/1 Knapsack problem.
- Illustrating the impact of incorrect space allocation in a store on Guest experience and sales.
- Highlighting the challenges faced by decision makers and how the Space Models address these issues.
- Description of the models used and an explanation using diagrams and formulas.
- Explaining how the recommendations generated by the models are utilized by decision makers to inform their space planning decisions.