Session: MVP Mindset for Data Science Leads
As data-science and machine-learning team leaders, we face the conflict between quick delivery and the uncertainty of the ML experimental nature. In this talk, Topaz will cover methodologies for improving focus, reducing uncertainty, and boosting your ability to transform algorithmic research into production.
Using case studies from real-world data domains, this session covers data subsets, inner releases, data-driven scrum, and test-driven machine learning:
Join the talk to learn about minimal viable product (MVP) and the different application aspects in your data science and machine-learning research cycles.
Bio
Topaz Gilad is an R&D manager specializing in AI, machine learning, and computer vision, leading production-oriented innovative research.
With experience in large companies as well as startups, in various industries, from space imaging and semiconductor microscopy to sports tech, and the wellness industry, she has developed methodologies to scale up while improving quality, delivery, and teamwork.
Currently VP of AI and Algorithms at Voyage81, ODDITY’s innovation core for vision-based AI. Previously head of AI at Pixellot, a leading AI-automated sports production company.
Topaz is also an advocate for women in tech. When she is not building algorithmic teams, she enjoys painting.