Yael
Daihes
Chief Data Scientist and Cybersecurity Leader | Public Speaker
"Hit the (ML) road(map) Jack"
"Hit the (ML) road(map) Jack"
Fri May 23 - 8:10 AM EDT/New York (See in local time)
Add to Calendar
04/23/2024 8:10 AM
04/23/2024 08:50 AM
America/New_York
#WTGC2024
"Hit the (ML) road(map) Jack"
#WTGC2024
"Hit the (ML) road(map) Jack"
https://app.events.ringcentral.com/events/women-in-tech-global-conference-2024/reception
https://www.womentech.net/vfairs
"Hit the (ML) road(map) Jack"
"Hit the (ML) road(map) Jack"
Don’t miss out and join visionaries, innovators, and thought leaders from all over the world at the Women in Tech Global Conference.
Vote by Sharing
Unite 100 000 Women in Tech to Drive Change with Purpose and Impact.
Do you want to see this session? Help increase the sharing count and the session visibility. Sessions with +10 votes will be available to career ticket holders.
Please note that it might take some time until your share & vote is reflected.
Session: Hit the (ML) road(map) Jack
With the advancement of AI, it seems there isn’t a task unsolvable by this amazing technology, and no wonder every type of product wants to leverage it. You hire the most brilliant researchers and wait for the magic to happen, but apparently, not every scientist can translate a business problem into a research task, and not every business leader can vividly define achievable goals for the ML organization.Imagine a digital health company that holds electronic medical records of thousands of patients. I believe we would all immediately assume their ML organization is creating some form of a diagnostics model, to ultimately improve the patients’ health. But, is that necessarily the best way to improve someone's health given this type of data? Maybe it would be more helpful to predict the severity of each patient's case and offer prioritization? And who said that improving patients’ health was even the company's objective? Maybe their objective is to optimize the doctor’s clinic operation.
Therefore, it is key to master the art of understanding the business needs and translating them to an AI research project. In this talk I will share my technique for learning a new business from a data science point of view and provide the pillars for building a realistic roadmap for an ML organization in a way that optimizes the business’ needs.
Key Takeaways
- Alignment of Business Goals and AI Research
- Translation of Business Problems into Research Tasks
- Building Realistic Roadmaps for AI Projects
Bio
Yael Daihes is a data science leadership, strategy and research consultant helping businesses from all sectors build and manage successful DS/AI organizations and projects. Starting with an 8 years service in the Israeli defense forces, Yael pursued a successful data science career within the cybersecurity sector that included heading major R&D Data Science organizations both in the defense forces as well as in the private sector (one of which sold for 560M$). Yael holds a BS.c in computer science from the Hebrew University and a MS.c in Software and Information systems engineering from Ben Gurion university with a specialty in machine learning and big data systems. In her spare time she volunteers with “Baot”, Israel's largest “females in R&D” community, managing one of their programs as well as mentoring fellow females in the industry.