What we're about

It's time to create a meetup in Milan for academics, hobbysts or professionals interested in Machine Learning solutions and latest development of AI.

The idea behind this meetup is to strive for the technological and innovative aspects, in contrast to the commercial part. The vision is to bring ML Milan at an European level and to build a community of AI practitioners in Milan.

Feel free to get in touch if you are interested to take part in the organizational setup or if you have a topic you want to talk about: we're always looking for interesting presentations, anything from 10 up to 30 minutes. Please, remember that talks shouldn't be commercial: the idea is to build a meetup around people passionate about AI & ML, not to promote some specific product or service.

This meetup is no-profit with talks, held in English, regarding:
- ML applications in the industry with real case examples
- new papers and software features

Feel free to follow us in our official Twitter channel: https://twitter.com/ML_Milano

If you want to propose a talk to our group or contact us, send us an email at machinelearningmilan@gmail.com

If an organization would like to host it, or sponsor food & drink, feel free to get in touch.

Upcoming events (1)

#36 MLMilan: Conversational agents and inventory optimization

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Dear all,

ML Milan is back in December where we will explore two use cases, one on conversational agents for e-commerce and one on inventory optimization. The event will be held online via Zoom and the talks will be held in English.

Talk 1: “From Product Searches to Conversational Agents for E-Commerce”
As consumers’ demand for online shopping substantially increased in the last few years, e-commerce companies are still far from providing high-quality user experiences that may compete with in-store experiences. On the one hand, matching search queries with highly relevant products for discovery and browsing is still a challenge within existing search technologies. Available e-commerce solutions hardly provide tools to optimize product search relevance and fail to integrate user behavior signals into the search optimization pipeline. On the other hand, accessing the rich and complex information concealed in an e-commerce catalog through a search bar has not evolved far since its initial adoption.
In this talk, we illustrate how the VUI conversational AI platform has been successfully adopted to both improve the user’s experience quality with highly relevant search and discovery results and expand the traditional search bar with conversational agents’ technology, enriching the user’s experience at each stage of the e-commerce product life cycle. We review in depth some of the key deep learning models as part of the query understanding component and discuss the overall conversation architecture as it integrates with an existing e-commerce catalog. We include real-life demonstrations derived from use cases extracted from deployed systems.
Giuseppe "Pino" Di Fabbrizio, VUI
Pino Di Fabbrizio is VUI, Inc.’s Chief Technology Officer and co-founder. Before VUI, he was a principal research scientist and group leader at the Rakuten Institute of Technology in Boston. He was previously a senior research scientist at Amazon Alexa Science and a lead research scientist at AT&T – Labs Research. His research interests and publication topics include conversational agents, machine learning, natural language understanding, natural language generation, and large-scale speech system architectures. He published more than 80 papers and was awarded 32 patents. As a senior IEEE member, he regularly contributes to international scientific committees and editorial boards.

Talk 2: “Transformer-based entity matching for inventory optimization in manufacturing at scale”

Descriptive text is often the only source of available information which can be used to identify components in the manufacturing industry. This prevents large companies, especially multinationals, from keeping a centralized inventory of the thousands of items that are used in their production plants, such as replacement parts. This talk describes how modern transformers-based architectures can be applied to entity matching using plain text descriptions at scale, and in turn to improve inventory management, and spend analysis.

Francesco Battocchio, Anchormen
Francesco Battocchio is a Senior Data Scientist at Amsterdam based consultancy company Anchormen, where he leads the development of AI solutions for clients in consumer goods, manufacturing, and pharmaceutical industry. He is passionate about applying cutting edge machine learning and deep learning solutions to real life problems. He has been previously working as a Lead Data Scientist in the energy sector in Abu Dhabi, as a Data Scientist in London, and as a Research Scientist in Cambridge. He holds a PhD in Mechanical Engineering from the University of Cambridge, a MSc in Material Science and a BSc in Mechanical Engineering from the University of Trento.

Where? Join us on Tuesday 13th of December on Zoom at this link:
https://zoom.us/j/96733262253?pwd=eGNTbTZlOVFFTUVIamtVdmhKd3RWZz09. The event will start at 18:45 and will finish around 20.00

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