Session on "Use Cases and Risks of ML in Capital Markets" | 23rd Dec at 4pm Hi everyone! The AI and Risk Mitigation project is well underway and for the third session, we will be joined by Rachna Maheshwari, Associate Director at CRI… more
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.
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 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.
Adaptive Metric Alignment for Demand Forecasting in Swiggy Instamart
Link to presentation: https://docs.google.com/presentation/d/1ZaA3TdTqBHurUJV7ngEhxVvZOkTM5D0vMI21qdI48kI/edit#slide=id.p1
Instamart, the quick commerce grocery delivery service of Swiggy gives unparalleled convenience of being able to order, from a huge assortment, across fresh fruits & vegetables/ dairy/FMCG products and accessories for household requirements, parties or festivities, pretty much at any time of the day and also through late night (from 6am to 3pm) and get the delivery in ~10–15 min. Instamart follows a dark store model where micro-fulfilment centers are established to fulfill the grocery orders of a certain geographical area of a few kilometers of radius. Efficient demand planning ensures that the sufficient units of each of the products are ‘available’ in the closest pod (dark store) for customers to order throughout the day, while making sure not stocking up too many units which can eventually lead to ‘wastage’. For efficient planning, ML based forecasting techniques are used to predict the daily ‘demand’ of an item for a given store (referred as SKU). But the demand forecasting for Instamart, or instant grocery delivery systems in general, have a handful of challenges that traditional forecasting methodologies can not resolve.
Firstly, due to the hyper-local nature of demand planning, there is high variation of demand across geographies, items and days – which leads to frequent ‘out-of-stock’ for some of the SKUs even before the pod is closed for the day. Hence, the historically observed time series data for building forecasting models is not the accurate representation of the ‘true’ demand, rather it is a truncated demand. The frequent absence of true demand makes the model development and evaluation challenging, especially when we are dealing with number of SKUs in the order of 10^4.
To track the efficiency of the demand planning, the business team tracks two metrics primarily: 1) availability – it measures the proportion of the day a SKU was available for the users to order, and 2) wastage – it approximately quantifies the units over-stocked and eventually led to wastage. Not being able to accurately evaluate the model performance using traditional metrics such as wMAPE on back testing data can lead to deploying forecasting models in production that can either underpredict or overpredict the true demand which means lower availability (i.e., revenue opportunity loss) or higher wastage respectively.
In this presentation we will go over our approach of ‘Adaptive Metric Alignment’ for accurate model evaluation which is more closely aligned with the business metrics. We will cover the following topics in our presentation:
- Introduction to demand planning process for Swiggy Instamart
- Demand forecasting challenges for quick commerce grocery delivery services
- Drawbacks of traditional model evaluation methods
- Adaptive metric alignment: estimated availability and wastage
Production implication and conclusion