The largest-ever virtual summit for machine learning developers recently concluded after three action-packed days of tech talks, workshops, paper presentations, networking, and awards.
The team from dunnhumby, one of the fastest growing customer data sciences compes in India, received remarkable recognition at the Machine Learning Developer Summit (MLDS 2021) hosted by Analytics India Magazine in February.
Sanjay Shukla from dunnhumby was named as one of the '40 under 40' data scientists in India at the summit. The award recognizes the innovators and achievers of the analytics industry. Currently Sanjay is working as Director - Data Science, Media and Customer Engagement where he leads the development and deployment of personalization science in retail. He has published multiple research papers on optimisation and machine learning approaches in peer-reviewed journals.
"It's is an honour to receive the 40 under 40 Data Scientist award. This award is a true testimony of the cutting-edge AI and ML solutions that we develop for our customers in dunnhumby. I think the real power of AI and ML gets unleashed when data, science and engineering comes together at scale. In dunnhumby, we are doing it very efficiently and improving shopping experiences for millions of people across the globe," - Sanjay Shukla.
Along with this, a research paper from dunnhumby's Neeraj Mishra was selected in the Top 10 papers presented at the conference. The topic of his paper was Predicting Missing Product Taxonomy in Retail: An Embedded Approach Using N-gram Mixture Models and Newton's Method. Neeraj developed a novel machine learning algorithm to predict product taxonomy by leveraging N-gram Mixture Model, cross-entropy function, and Newton's optimisation method.
"MLDS was a great platform to present the innovation that we do in dunnhumby. I am thankful to dunnhumby India leadership for providing all the tools needed to push boundaries and bring out tailor-made solutions for our customers," - Neeraj Mishra.
Seema Mudgil's paper on Product Based Store Clustering and Range Recommendation was also selected as one of the presenting papers for this summit. In her paper, she provides a store clustering solution that facilitates customer-centric assortment.
A key highlight of the summit was an exclusive workshop by Dr. Anthony Kilili, Head of Science at dunnhumby, onMachine learning gone wrong: Common mistakes and challenges in bringing ML into production. The session received rave reviews from over 300 attendees coming from top technology, consulting, and data science firms.
In the workshop, Anthony shared his experience on the challenges he faced in various stages of the ML project lifecycle. The workshop raised awareness for ML practitioners to be better equipped to build sustainable ML systems with emphasis on criticality of collaboration among various stakeholders such as product managers, data engineers, data scientists, DevOps engineers, and MLOps engineers.
Rochak Khanna and Mukul Sabharwal jointly ran a Tech Talk onDiscovery of a customer journey and uncovering the potential purpose of a shopping visit. They presented various unsupervised ML algorithms that can identify specific visit purposes by extracting the latent/hidden factors within data.
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( With inputs from ANI )
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