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OUR SPEAKERS
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Noa Weiss is an AI & machine learning consultant who’s been working with data for over a decade. She works with both early-stage startups and established companies, helping them design their AI strategy and build the ML projects that best fit their needs. She also leads the Israeli community of Women in Data Science, utilizes deep learning for the preservation of whales with the Deep Voice Foundation, and is passionate about animal welfare and about promoting female entrepreneurship.
Read more at www.weissnoa.com.
Noa Weiss
AI & Machine Learning Consultant
English, Hebrew
Languages:
Location:
Haifa, Israel
Can also give an online talk/webinar
Paid only. Contact speaker for pricing!
MY TALKS
The Unspoken Problems With Machine Learning in Security
Data / AI / ML, Security / Privacy
Machine Learning is the hottest buzzword. Everybody loves it, everybody sells it. But why is it that while fields such as Computer Vision or Natural Language Processing have stellar achievements, with new record-breaking models published every other week, the Cybersecurity industry staggers behind?
Are Anomaly Detection algorithms – so well beloved for the prevention of attacks and of fraud - really suitable for those intended purposes? What price do we pay for keeping things hushed? Where do our huge datasets fail us? And how might we try and solve that?
In this talk I will go over several points that hold us back, among them: our rapidly changing input data, and who’s to blame for it; the known issues of imbalanced & untagged datasets, and why our solutions for them are insufficient; and, finally, the biggest culprit: the confidential nature of our field, and how it keeps us from being great.
The unspoken problems with Machine Learning in security: let’s talk about them.
10 Must-Know Concepts in Machine Learning
Data / AI / ML
Do you often find yourself in meetings with obscure machine learning terms thrown all around you, and no idea what they mean? There is hope yet. Designed for tech professionals, this talk introduces 10 must-know concepts in ML, giving you the basic tools to better understand the work around you. Rock the next water cooler chat with your newfound knowledge of these fundamentals of machine learning!
Real-World Problems With Real-World Data
Data / AI / ML
Machine learning courses are all fun and games, but once you hit your first real job, new problems start rolling in. What to do if you don't have enough data? Who might listen to your plea for more "ground truth" labels? And what does this all have to do with a vacation in the tropics? Join Noa Weiss, an AI & Machine Learning consultant, to hear more about those real-world problems that come with real-world data, and how to tackle them like a pro.
Choosing the Right Machine Learning Abstraction for your Business Needs
Data / AI / ML
Choosing the right abstraction for your problem - it is a step crucial to the success of every ML project, yet one that is often overlooked. We could spend hours debating whether to use XGBoost or CATboost, yet neglect giving our conscious attention to a more elementary decision: how to model our problem in the first place.
The exact same business case could be modeled, for example, as a classification problem, a clustering one, or even a graph link-predictions task. And as the options vary, so do the considerations for choosing among them, including not only machine learning theory, but also the needs of your business, organizational constraints, and many more.
Join me to discuss the principles of choosing the best ML abstraction for your needs, things to consider and pitfalls to avoid, all through the lens of a real-life case study from my consultancy work.
The Quick & Dirty AI Startup
Data / AI / ML, Entrepreneurship
You just founded your AI startup - and now it’s time to build your algorithm. But, beware: when building an AI-based product from scratch, it’s easy to get lost in the details, investing effort where it’s not needed.
Join me to hear of simple ways to save R&D time without compromising on product quality, gathered from my experience consulting for multiple early-stage startups. We’ll discuss what’s important to focus on when building your algorithms, where you want to invest your time, and what corners you should cut. We will also talk about the right time to hire an in-house data scientist, and how to make do before that.
If you’re thinking of starting your own AI startup, already have one, or are a sole data scientist in a brand-new venture - this talk is for you.
The Unspoken Problems With Machine Learning in Security
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10 Must-Know Concepts in Machine Learning
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Real-World Problems With Real-World Data
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Choosing the Right Machine Learning Abstraction for your Business Needs
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The Quick & Dirty AI Startup
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