Save the date!

📆 July 9
⏰ 8 PM Israeli time
📹 Zoom
👉 Join the webinar

See you there!

On June 25, we’re diving into this topic in a live webinar hosted by Nebius Academy — educational project of Nebius (an AI-centric public cloud platform).

Meet your speaker — Rod Rivera
DevRel at Jentic, AI Product Engineer, Professor of AI at ITAM, and Influencer.

Rod will walk you through:
📌 How to create agentic workflows that are not only machine-readable and testable — but also easy to understand.
📌 How open standards like OpenAPI and Arazzo help design, orchestrate, and maintain complex AI systems.
📌 How open standards improve debugging, observability, and extensibility in multi-agent systems.

Expect real-world examples and an intro to key tools.
And get your questions ready for Q&A!

📆 June 25
⏰ 8 PM Israeli time
📹 Zoom

👉 Join the webinar

See you there!

Nebius, a leading AI-centric public cloud platform, runs a range of educational projects — both in-person, like Y-DATA, and online. As part of these initiatives, we’re now launching a new free course: LLM Engineering Essentials , designed for developers ready to start building with LLMs.

Join us online on May 7 at 8 PM (Israeli time) for a welcome session where you’ll get all the details and a full learning roadmap for the first guided cohort.

What’s ahead:
⌛️ 12-week program
🧑‍💻 Weekly expert-led sessions
🎓 Feedback and ongoing support from the team

Throughout the course, expert-led webinars will help you:
📌 Master LLM APIs, workflows, and agents
📌 Work with self-hosted LLMs and encoders — and fine-tune models
📌 Use web and database search to create great RAG applications
📌 Build your own service for custom AI-powered non-player characters
📌 Get your questions answered as you learn, code, and practice

Ready to dive in?
Join the welcome session!

* More info about new course by Y-DATA.

Agenda:
18:30 - 19:00 Registration, Mingling, Snacks & Beer
19:00 - 19:30 Lightning talks
Andrey Nikitin (Cyera) - No Limits: The File Level Model That Classifies Any Document
Urska Jelercic (Lightricks) - Beyond the Pixel: Identity Preservation in AI-Powered Image Editing
Udi Cohen (Vendict) - AI-Native Trust Building
19:30 - 20:00 Keynote by Gal Chechik (Nvidia) - Recent advancements in Visual Gen AI
20:15 - 21:00 Panel Discussion "The Future of Agents" with
Amit Mandelbaum (AI21), Uri Goren (Argmax), Shir Chorev (Deepchecks), Simon Karasik(Nebius), Serj Smorodinsky (Loris)

Abstracts:
"Recent advancements in Visual Gen AI"
Between training and inference, lies a growing class of AI problems that involve fast optimization of a pre-trained model for a specific inference task. These are not pure “feed-forward” inference problems applied to a pre-trained model, because they involve some non-trivial inference-time optimization beyond what the model was trained for; neither are they training problems, because they focus on a specific input. These compute-heavy inference workflows raise new challenges in machine learning and open opportunities for new types of user experiences and use cases. In this talk, I describe two main flavors of the new workflows in the context of text-to-image generative models: few-shot fine-tuning and inference-time optimization. I'll cover personalization of vision-language models using textual-inversion techniques, and techniques for model inversion, prompt-to-image alignment and consistent generation. I will also discuss the generation of rare classes, and future directions.

"No Limits: The File Level Model That Classifies Any Document"
In this talk, we introduce the File Level Model, a novel solution for document classification that processes files such as Word documents and PDFs and assigns meaningful classifications—even for document types it has never encountered before. Traditional multi-class models are constrained by fixed class labels and can only recognize categories they have been explicitly trained on. By contrast, our File Level Model leverages a fine-tuned LLM, enabling it to adapt to a vast and evolving range of document types. We will discuss how this approach overcomes the limitations of predefined class sets, and showcase its real-world applicability in classifying new or specialized documents.

"Beyond the Pixel: Identity Preservation in AI-Powered Image Editing"
Inpainting, retouching, and content-aware edits powered by generative AI require more than just realism—they demand strict identity preservation. This talk will discuss recent work done at Lightricks Facetune research team that ensure AI-driven modifications remain faithful to original content, balancing flexibility with fidelity in creative applications.

"Vendict - AI-Native Trust Building"
Companies rely on vendors to drive innovation, but how can they be sure those vendors are secure and follow all the rules and best practices needed to protect their business and reputation?
Vendict makes collaboration easier by providing instant “trust but verify” assessments using specialized AI technology.
Our first product was built entirely on proprietary models. As LLMs and AI agents continue to advance, we continue to develop and integrate proprietary models at critical points in our pipeline, ensuring a competitive edge and a solution that truly works.

Y-DATA Meetup #22
Advanced Recommender Systems: Insights, Techniques, and Applications
Hosted by Taboola. Talks are in English. More info about Y-DATA.
Directions: Jabotinsky 2, Ramat Gan, Atrium Tower ( Floor 23)
Google meet link for those who gonna join remotely

Agenda:
18:00 - 18:30 Registration, Mingling, Snacks & Beer
18:30 - 18:45 Opening, Host and Organizer words.
18:45 - 19:15 "Waterfall Auction Recommendations with Dynamic Programming״
Dan Halbersberg, DS Team Lead at Playtika
19:15-19:45 "Intro to Vector Search"
Uri Goren, founder of Argmax
19:45-20:15 "Uncertainty estimation in large scale recommender systems"
Alexey Pechorin, Data Scientist at Taboola

Abstracts:

1. "Waterfall Auction Recommendations with Dynamic Programming"
Over the last two decades, an increase in advertisement slot sales from online auctions has led to significantly stronger revenues for publishers. One of the most relevant and widely used auction protocols is called waterfall strategy, through which the publisher makes sequential, pre-defined, ad-requests to ad-suppliers through third party ad networks. Each ad-request targets a specific ad network with a specific price. As different waterfall strategies potentially lead to diverse total revenue, it is extremely beneficial for the publisher to optimize the strategy they use. However, the number of possible waterfall strategies grows exponentially with the number of ad networks and prices in play, which makes waterfall optimization a formidable task. In this presentation, we talk about the dual nature of the waterfall strategy as both an auction protocol and a recommendation technique. We propose a dynamic-programming based algorithm that recommends modifications to the waterfall strategy, empowering media buyers to maximize their revenues. This method has been extensively tested using real-world data and industry A/B testing, outperforming state-of-the-art methods and even human experts

2. "A brief intro to vector search"
While recommendation systems can wear many forms, lookalike search became the most dominant approach.
At its core, similarity search depends on semantic representation and an efficient search.
In this talk, we would introduce vector similarity search and discuss how to control the trade off between accuracy and performance

3. "Uncertainty estimation in large scale recommender systems"
Taboola's recommender system utilizes a clickthrough rate (CTR) model to estimate ad revenue but aims to also output uncertainty estimations to manage risk-reward tradeoffs and drive feature exploration. Traditional uncertainty estimation involves training multiple models, which is costly. We examine two methods that approximate uncertainty with a single model: Variational AutoEncoder (VAE) inspired sampling layers and Mixture of Local Experts (MoLE) based approximations. Both methods, by introducing non-determinism and multiple model outputs respectively, showed high correlations, with MoLE providing superior results. This talk discusses these paradigms, their comparisons, and their pros and cons.

Speakers:

Dan Halbersberg received his Ph.D degree in Industrial Engineering and Management from Ben Gurion University of the Negev, Beer Sheva, Israel in 2020. Since then he was postdoctoral researcher at the same university, and senior data scientist at Playtika Al lab. Currently, Dan manages the AI-marketing group at Playtika’s AI lab. Dan has 12 years of experience, leading data science projects in the industry and academia and publishing papers in top journals and conferences. His main interest are graphical models, analysis of temporal longitudinal data and deep neural network.

Uri Goren is a machine learning expert, leading argmax.ml, a recommendation system consulting firm.
Uri has a rich experience, having worked at several fortune 500 companies such as Microsoft, Intel, AT&T as a data scientist, and founded 2 ML centered startups.
On his free time, Uri hosts Explainable, a ML podcast in Hebrew, and organizes the Tel Aviv chapter of the PyData community

Alexey Pechorin is a seasoned Machine Learning Engineer in Taboola’s Algorithms department, focusing on bidding algorithms to serve recommendations. Alexey possesses MSc in CS from Tel Aviv University.

The course begins on the 13th of April 2023, so hurry and register.
For more details and registration visit the course site: https://ydataskills.com/ds

The course begins on the 13th of April 2023, so hurry and register.
For more details and registration visit the course site: https://ydataskills.com/ds

The course begins on the 13th of April 2023, so hurry and register.
For more details and registration visit the course site: https://ydataskills.com/ds