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ICML-2021
Mon Jul 19 11:00 AM -- 02:15 PM (EDT)
Scientists in the field of machine learning (ML) – including deep learning (DL) -- aspire to build better models (usually judged by beating SOTA in well-defined tasks and datasets); successful applications of such models, on the other hand, are about product-market fit (PMF) in environments with ever-growing complexities. As many expect ML to play a bigger role in our society, ML scientists’ ability to influence this journey will depend on putting ML research in a PMF context and vice versa (i.e., optimizing for market.fit()+⍺*model.fit(), instead of optimizing for model.fit() alone). Therefore, in this tutorial we aim to cover the general principals of building AI products in the “real world”, covering topics such as product design/management, achieving product-market fit, and ML R&D in this context.
Scientists in the field of machine learning (ML) – including deep learning (DL) -- aspire to build better models (usually judged by beating SOTA in well-defined tasks and datasets); successful applications of such models, on the other hand, are about product-market fit (PMF) in environments with ever-growing complexities. As many expect ML to play a bigger role in our society, ML scientists’ ability to influence this journey will depend on putting ML research in a PMF context and vice versa (i.e., optimizing for market.fit()+⍺*model.fit(), instead of optimizing for model.fit() alone). Therefore, in this tutorial we aim to cover the general principals of building AI products in the “real world”, covering topics such as product design/management, achieving product-market fit, and ML R&D in this context.
Session 2
Session 2
(30 mins)
(30 mins)
Product-Market Fit (R. Khorshidi)
Session 3
Session 3
(15 mins)
(15 mins)
Build Measure Learn (R. Khorshidi)
Session 4
Session 4
(30 mins)
(30 mins)
Experiments and Metrics (R. Khorshidi)
Session 5
Session 5
(45 mins)
(45 mins)
Example (P. Faratin)
Q&A
Q&A
(30 mins)
(30 mins)
Schedule
Schedule
Session 1 (15 mins): Overview of tutorial and the core idea (R. Khorshidi)
Session 2 (30 mins): Product Market Fit (R. Khorshidi)
Break (15 mins)
Session 3 (15 mins): Build Measure Learn (R. Khorshidi)
Session 4 (30 mins): Experiments and Metrics (R. Khorshidi)
Break (15 mins)
Session 5 (45 mins): Examples (P. Faratin)
Q&A (30 mins)
Speakers
Speakers
Reza Khorsidi, D.Phil. (Oxon)
Reza Khorsidi, D.Phil. (Oxon)
Reza is currently the chief scientist at AIG, and a PI (in ML and Biomedicine) at The University of Oxford's Deep Medicine program.
His current research at Oxford is focused on probabilistic machine learning, and neural sequence models, for biomedical informatics, population health, and precision medicine; more specifically, he is interested in using ML for the development of personalised health predictions/recommendations, plus achieving an improved understanding of multimorbidity.
Reza’s team at AIG (i.e., Investments AI) is a group of scientists, engineers, and product designers/managers, focused on the development of AI-first products in the FinTech space.
Peyman Faratin (PhD)
Peyman Faratin (PhD)
Peyman is currently founder of RobustLinks and Advisor/Investor in Parameter Ventures.
Peyman trained as a computer scientist, receiving his doctorates in field of (distributed) AI, machine learning and game theory from University of London in 2000. He has over twenty years of experience in design and implementation of AI and ML systems in finance, telecommunication, media, health and agriculture. He is currently founder of RobustLinks, building and delivering Natural Language Processing and ML technologies and products for array of sectors. Prior to that he served as the Chief Scientist at Two Sigma’s Insurance Quantified, building risks models using third party datasets and ML. He was research scientist at MIT (Computer Science and AI Lab) .
Peyman trained as a computer scientist, receiving his doctorates in field of (distributed) AI, machine learning and game theory from University of London in 2000. He has over twenty years of experience in design and implementation of AI and ML systems in finance, telecommunication, media, health and agriculture. He is currently founder of RobustLinks, building and delivering Natural Language Processing and ML technologies and products for array of sectors. Prior to that he served as the Chief Scientist at Two Sigma’s Insurance Quantified, building risks models using third party datasets and ML. He was research scientist at MIT (Computer Science and AI Lab) .
He is a board member of number of emerging startups, as well as member of the Association of Computing Machinery, the New York City CTO club, New York Academy of Sciences and a visiting academic at Courant Institute of Mathematics at NYU. Dr. Faratin is an active member of the NYC technology community.
He is a board member of number of emerging startups, as well as member of the Association of Computing Machinery, the New York City CTO club, New York Academy of Sciences and a visiting academic at Courant Institute of Mathematics at NYU. Dr. Faratin is an active member of the NYC technology community.
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