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OUR SPEAKERS
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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, wellness, beauty, and self-care industry, she has developed methodologies to scale up while improving quality, delivery, and teamwork.
Currently VP of AI and Algorithms at Voyage81, an innovation company that excels in computer vision deep learning algorithms in both RGB and hyper-spectral domains. 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.
Topaz Gilad
VP of AI and Algorithms
English, Hebrew
Languages:
Location:
Tel-Aviv, Israel
Can also give an online talk/webinar
Paid only. Contact speaker for pricing!
MY TALKS
From Cost-Sensitive Classification to Regression: Unleash the True Potential of Your Labels!
Data / AI / ML
In the words of David Mumford: "The world is continuous, but the mind is discrete."
We often define categories when breaking down a real-world problem into an ML-based solution. However, especially in the medical domain, actual target values may be continuous or at least ordered. This is something to consider and even leverage in the design of your ML model.
Using case studies from real-world data domains, we will see how acknowledging the inner relations of our target labels can boost the knowledge we provide in the training phase, better model the world, reduce overconfidence, and improve robustness. From classical concepts to state-of-the-art, this talk will walk you through regression-based approaches for what may seem like classification problems. Unlock the true potential of your labels and boost your classifiers!
MVP Mindset for Data Science Leads
Data / AI / ML, Software Engineering, Soft Skills, Professional Development, Leadership
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 workshop, 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 workshop covers data subsets, inner releases, data-driven scrum, and test-driven machine learning:
Join the workshop to learn about minimal viable product (MVP) and the different application aspects in your data science and machine-learning research cycles.
Bon Voyage! Leading Machine Learning Research Journeys With Happy (Into-Production) Endings
Data / AI / ML, Soft Skills, Leadership
Why is the process of transforming research into a “real world” product so full of question marks? We often know where the research journey starts but have uncertainty about how and WHEN it ends.
In this talk, I will share my own experience leading algorithmic teams through the cycle of research into the production of live-streaming AI products. I will also share how to mitigate between agile, incremental delivery, and giant leaps forward that require longer research. The talk will outline test-driven machine learning development. Understanding the minimum viable product (MVP) way of thinking can help not only managers but every developer. Learn to outline MVP for new AI capabilities and move forward with production in mind, while always raising quality standards. At the end of this session, you will get the boost you need to take the data-driven experimental mindset to the next level, spiced with methodologies you can adapt to development as well as research.
Keep Rolling: Strategies for AI Models Rollout from POC into an MVP
Software Engineering, Data / AI / ML, Product
In today’s economic climate, AI groups are asked to show deliverable value and not only the promise of innovation. As data science and machine-learning managers, we face the conflict between quick delivery and the uncertainty of the ML experimental nature. In this talk, Topaz will cover methodologies to transform your proof-of-concept (POC) into an actual minimal-viable product (MVP) release.
This talk will lead your way to map your own MVP strategy and the path for deployment. Covering ways to improve focus and reduce uncertainty: from data subsets to test-driven machine-learning. On top of those, version release strategies and when each should be applied to reduce risk while ensuring value.
From Cost-Sensitive Classification to Regression: Unleash the True Potential of Your Labels!
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MVP Mindset for Data Science Leads
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Bon Voyage! Leading Machine Learning Research Journeys With Happy (Into-Production) Endings
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5
Keep Rolling: Strategies for AI Models Rollout from POC into an MVP
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5
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