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Machine Learning is the hottest buzzword. But why is it that while fields such as Computer Vision or NLP have stellar achievements, the Cybersecurity industry staggers behind? 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, once we spot these issues, might we try and solve them? 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.

In this episode, Dean speaks with Noa Weiss, the wonderful AI & ML consultant. They dive into Deep Learning research for marine mammal sounds, abstractions for machine learning projects and some of the unspoken challenges she's seen in the ML development process. Also prediction markets and harry potter. Join our Discord community: https://discord.gg/tEYvqxwhah - Timestamps: 0:00 Intro 1:24 Deep Learning research with whale sounds and how it's related to swing dancing 4:28 Why thinking about data projects as machine learning projects might be wrong 8:55 The difference between ChatGPT and GPT-3 10:41 Naive solutions in ML problems 11:24 Understanding where your company stands on the spectrum of solutions to ML problems (between an if/else statement to a sophisticated deep learning solution) 15:11 How do you think about developing ML models that will be deployed to production? 18:13 The unspoken challenges in the ML development process - surprisingly, tools aren't one of them :) 25:00 Choosing the right abstraction for your machine learning project 28:43 Noa's day-to-day – being a machine learning consultant 30:17 Machine learning in FoodTech and the Alternative Protein space 33:10 Getting started with data science in the FoodTech industry? 36:22 Predictions about the field of ML and MLOps 40:27 Rationality and whether or not predictions are useful 42:00 What do you think is true, but few people agree about ML or data science? 44:11 Recommendations for the audience Relevant Links: Noa's talks: 🎥 The Quick & Dirty AI Startup: https://www.youtube.com/watch?v=HZ_LRxP3ep0 🎥 Choosing the Right Machine Learning Abstraction for your Business Needs – https://www.youtube.com/watch?v=C0gN47H91HM ➡️ Noa Weiss on LinkedIn – https://www.linkedin.com/in/noa-weiss/ ➡️ Noa Weiss on Twitter – https://twitter.com/NWeiss 🌐 Noa's website: https://www.weissnoa.com Recommendation Links: - Unsong – https://unsongbook.com/ - Harry Potter & The Methods of Rationality – https://www.hpmor.com/ 🌐 Check Out Our Website! https://dagshub.com Social Links: ➡️ LinkedIn: https://www.linkedin.com/company/dagshub ➡️ Twitter: https://twitter.com/TheRealDAGsHub ➡️ Dean Pleban: https://twitter.com/DeanPlbn

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 with Ex Libris. === Noa Weiss is an AI & Machine Learning Consultant. This talk was given live at the "Level Up Your Game" meetup by WiDS Israel, at the Israeli office of Ex Libris, October 2022. === Women in Data Science is an international community, aimed at female-identifying professionals in the field of data science. You can find the Israeli group here: www.facebook.com/groups/2032669270341585

Keynote recorded live at #datalift22 Summit Berlin 22.-24. Speaker: Noa Weiss, AI & Machine Learning Consultant Title: The Quick & Dirty AI Startup You just founded your AI startup - and now it’s time to build your algorithm. 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. If you’re thinking of starting your own startup, already have one, or are a sole data scientist in a brand-new venture - this talk is for you. Noa Weiss is an AI & machine learning consultant working with data for over a decade. She helps startups and larger companies use their data to drive business success. Noa leads the Israeli community of Women in Data Science, and is passionate about FemTech and promoting female entrepreneurship.

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, once we spot these issues, might we try and solve them? 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. Language - English Noa Weiss weissnoa.com Noa is an AI & machine learning consultant who’s been playing with data for over a decade, both in academia and in the tech industry. Prior to freelancing, Noa explored the world of risk & security, working for companies such as Armis and PayPal, as well as consulting startups on fraud prevention. In her spare time she leads the Israeli community of Women in Data Science, utilizes deep learning techniques to improve whale research at the Deep Voice foundation, and mentors junior data scientists taking their first steps in the field.

WIT Demystifying Data Science Conference 2020 Noa Weiss, Data Science & Machine Learning Consultant, about 10 Machine Learning Concepts Every Startup Founder Must Know. ⠀ 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!

Details DataNights - Data Science Work in the Real World #5: Real-World Problems With Real-World Data ✔ ----------------------------------------------------------------------------------------------- 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.

https://www.greeninvoice.co.il/ 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 unfamiliar with data science, 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. Noa Weiss is a data science & machine learning consultant who’s been playing with data for nearly a decade, both in academia and in the tech industry. Prior to freelancing, Noa explored the world of risk & security, working for companies such as Armis and PayPal, as well as consulting startups on fraud prevention. In her spare time she leads the Israeli community of Women in Data Science, and mentors junior data scientists taking their first steps in the field.

Noa is a freelance data scientist who’s been playing with data for nearly a decade. Prior to freelancing, Noa explored the world of risk & security, working for companies such as Armis and PayPal, as well as consulting startups on fraud prevention. She also leads the data science track of Open Knesset, a project to promote transparency in the Israeli parliament. In her spare time she leads the Israeli community of Women in Data Science, is active in the local LGBTQIA+ tech community, and mentors junior data scientists.