Presentation: Machine Learning: Predicting Demand in Fashion

Track: ML in Action

Location: Cyril Magnin III

Duration: 2:25pm - 2:35pm

Day of week: Tuesday

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Abstract

Apparel/fashion retailers often have to buy inventory more than a quarter in advance and so have to make bets on the total demand that they expect to see in the relevant season. Also, the set of products offered by the brands change every year, and even the historical demand for previous season’s products is known only partially as each product is carried in only a subset of the stores.
In this talk, we will show how we (at Celect) use the historical data (point of sales transaction, inventory, product attributes, product images, product descriptions) to build a SaaS solution that helps buyers and merchants predict the future demand of products for the upcoming season. The short talk will cover the real life problem statement, high level ML frameworks, and how the product is used by buyers and merchants.

Note: This is a short talk. Short talks are 10-minute talks designed to offer breadth across the areas of machine learning, artificial intelligence, and data engineering. The short talks are focused on the tools and practices of data science with an eye towards the software engineer.

Speaker: Ritesh Madan

VP Engineering @celect

Ritesh leads the Engineering team at Celect. His team is responsible for building the pDB (predictive Database) platform that provides a database like abstraction to enable predictive queries and machine learning at scale. The pDB platform powers Celect's predictive analytic applications across the two main verticals at Celect: omni-channel retail and federal intelligence. Prior to Celect, Ritesh has led enterprise grade wireless networking products at Cisco, Accelera (now PlumeWifi), and Qualcomm. He has published extensively in optimization, machine learning, and wireless networking, and is a co-inventor on 65 patents. He holds a Ph.D. from Stanford and a BS from IIT Bombay, all in Electrical Engineering.

Find Ritesh Madan at

Tracks

  • Deep Learning Applications & Practices

    Deep learning lessons using tooling such as Tensorflow & PyTorch, across domains like large-scale cloud-native apps and fintech, and tacking concerns around interpretability of ML models.

  • Predictive Data Pipelines & Architectures

    Best practices for building real-world data pipelines doing interesting things like predictions, recommender systems, fraud prevention, ranking systems, and more.

  • ML in Action

    Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud

  • Real-world Data Engineering

    Showcasing DataEng tech and highlighting the strengths of each in real-world applications.