Agenda

08:30-09:30

Registration & Breakfast

Rovina | Hebrew
09:30-09:50

Opening Session

Rovina | Hebrew
09:50-10:20

Keynote - How to Build a Self Serve Data Platform?

“Data is the new oil” – a quote attributed to Clive Humby explains that, like oil, data is valuable, but if unrefined it cannot really be used.
Providing access to high quality data means nothing, if organizations don’t know what to do with it.
The ability to derive meaning from, and use data, creating value is directly related to the autonomy of the data consumers.
We will explore how to create an analytics stack that integrates applications that collect, transform and expose the value of data.

Denise Schlesinger

Principal Cloud Solution Architect, Microsoft

Denise Schlesinger is a Principal Solution Architect at Microsoft. She loves solving complex problems with the right architecture, building distributed applications that can scale. She believes in enabling organizations to make decisions based on data.

Denise Schlesinger is a Principal Solution Architect at Microsoft. She loves solving complex problems with the right architecture, building distributed applications that can scale. She believes in enabling organizations to make decisions based on data.

Rovina | Hebrew
10:25-10:55

The Data Swamp Face Off: Understanding the Interplay Between Different Data Roles in an Organization

In the data world, there are different types of roles that have different responsibilities and expectations. Data producers are those who generate, collect, or provide data for various purposes. Data consumers are those who use, analyze, or derive insights from data to support decision making or action. Data enablers are those who facilitate, manage, or optimize the data flow and quality between data producers and consumers.
Understanding the interplay between these roles is crucial for creating a successful data strategy and delivering value from data. In this panel, we will explore the challenges and opportunities that arise from the interactions between these players.

Moderator: Doron Porat

Director of Infrastructure, Yotpo

Doron Porat is the Director of Infrastructure at Yotpo, managing teams focused on Backend, Frontend, and Data Infrastructure. Doron has been practicing Data and leading Data teams for the past 12 years and joined Yotpo as a Data Infra Team Lead back in 2016. Doron is deeply passionate about the data world and believes that infrastructure plays a vital role in driving innovation and scaling engineering organizations. Her approach involves reducing the cognitive load for generalist developers by providing them with abstractions and generators, and ensuring that tooling is updated and aligned with the technical and cultural challenges of the organization. In addition to her work, Doron co-hosts a popular podcast in Hebrew called "The Data Swamp," which explores all aspects of data alongside her good friend and fellow data expert, Liran Yogev. In her free time, Doron LOVES grooming plants and forces herself to go on long runs in the park.

Doron Porat is the Director of Infrastructure at Yotpo, managing teams focused on Backend, Frontend, and Data Infrastructure. Doron has been practicing Data and leading Data teams for the past 12 years and joined Yotpo as a Data Infra Team Lead back in 2016. Doron is deeply passionate about the data world and believes that infrastructure plays a vital role in driving innovation and scaling engineering organizations. Her approach involves reducing the cognitive load for generalist developers by providing them with abstractions and generators, and ensuring that tooling is updated and aligned with the technical and cultural challenges of the organization. In addition to her work, Doron co-hosts a popular podcast in Hebrew called "The Data Swamp," which explores all aspects of data alongside her good friend and fellow data expert, Liran Yogev. In her free time, Doron LOVES grooming plants and forces herself to go on long runs in the park.

Moderator: Liran Yogev

Director of Engineering, ZipRecruiter

Liran Yogev has been a member of the big data community for over 7 years. Before joining Ziprecruiter as Director of Engineering, he spent several years at Yotpo developing innovative data platforms, ML products, and complex data-driven backend applications. In his current role, Liran leads the platform engineering domain at Ziprecruiter, focusing on ML platforms, data, and experimentation. Liran is passionate about solving complex problems and creating impactful infrastructure that increases the velocity of the organizations he works for. In addition to his work, Liran co-hosts a popular podcast in Hebrew called "The Data Swamp", which explores all aspects of data alongside his good friend and fellow data expert, Doron Porat.

Liran Yogev has been a member of the big data community for over 7 years. Before joining Ziprecruiter as Director of Engineering, he spent several years at Yotpo developing innovative data platforms, ML products, and complex data-driven backend applications. In his current role, Liran leads the platform engineering domain at Ziprecruiter, focusing on ML platforms, data, and experimentation. Liran is passionate about solving complex problems and creating impactful infrastructure that increases the velocity of the organizations he works for. In addition to his work, Liran co-hosts a popular podcast in Hebrew called "The Data Swamp", which explores all aspects of data alongside his good friend and fellow data expert, Doron Porat.

Ofir Ventura

Data & ML Engineering Team Lead, Lemonade

Ofir is a ML & data infrastructure engineering manager at Lemonade, leading the ML and data platform teams at Lemonade. Her expertise is in the fields of big data stacks, machine learning infrastructures, backend and databases.

Ofir is a ML & data infrastructure engineering manager at Lemonade, leading the ML and data platform teams at Lemonade. Her expertise is in the fields of big data stacks, machine learning infrastructures, backend and databases.

Dov Ziskin

Head of Business Intelligence, Pagaya

Dov is the Head of BI at Pagaya, with over 10 years of experience in the big data and analytics domain. He specializes in building complex analytical platforms using modern technology that provides significant business value. Over the past two years, Dov has built the BI group at Pagaya from the ground up, establishing an analytical platform that serves hundreds of data consumers across multiple departments in the company.

Dov is the Head of BI at Pagaya, with over 10 years of experience in the big data and analytics domain. He specializes in building complex analytical platforms using modern technology that provides significant business value. Over the past two years, Dov has built the BI group at Pagaya from the ground up, establishing an analytical platform that serves hundreds of data consumers across multiple departments in the company.

Yarin Benado

Director of Engineering (Data Platform and Insights), Gong

Yarin Benado is director of engineering at Gong, the leading revenue intelligence platform. At Gong, Yarin's main areas of focus are data platform and in-app analytics Before joining Gong, Yarin co-founded Vayo, a start-up company that helped SaaS organizations to make Sense of their customer data by turning complex cross-platform data into actionable insights. Vayo was acquired by Gong in September 2020.

Yarin Benado is director of engineering at Gong, the leading revenue intelligence platform. At Gong, Yarin's main areas of focus are data platform and in-app analytics Before joining Gong, Yarin co-founded Vayo, a start-up company that helped SaaS organizations to make Sense of their customer data by turning complex cross-platform data into actionable insights. Vayo was acquired by Gong in September 2020.

Ayelet Bergman

Strategic Business Analysis Team Lead, Wix.com

Ayelet Bergman leads the strategic analysis team at Wix, the leading website building platform. Her team helps Wix management in strategic decision making, by providing business insights and recommendations. Ayelet holds a B.Sc. in Computer Science & Economics from Tel Aviv University.

Ayelet Bergman leads the strategic analysis team at Wix, the leading website building platform. Her team helps Wix management in strategic decision making, by providing business insights and recommendations. Ayelet holds a B.Sc. in Computer Science & Economics from Tel Aviv University.

10:55-11:10

Coffee Break

ROVINA | HEBREW
11:10-11:40

Protecting Privacy in the Kingdom of Data: A Guide for Data Engineers

If data is king, then privacy is its crown jewel. Over the last two decades, I’ve worked as a technical leader in the data domain and have experienced first hand the importance of balancing the collection of valuable data with personal privacy protection.

I will examine real-life examples of privacy violations to emphasize the importance of privacy for data owners. Towards achieving privacy compliance, I will teach:
Different techniques to safeguard personal information, including anonymization, deliberate data decay, and differential privacy.
How and where these techniques could be applied
Additionally, the talk will explore current privacy threats and the role of data engineers in ensuring privacy and security in handling personal and sensitive information.

By the end of the talk, you will understand privacy challenges and learn practical solutions you can apply in your work. I believe that with data becoming increasingly central to our lives, privacy and data are equally important in the kingdom of data management.

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Inna Weiner

Senior Engineering Manager, Google

Inna Weiner is a senior technical leader with 20+ years of global experience. A big data expert, specializing in data processing, storage, access, insider risk, privacy and policy compliance. During her 15 years at Google, Inna led engineering teams in consumer and enterprise products including Google Sites, Google Search and Google Analytics. Most recently she led the Google Search Analytics Data Warehouse, growing her organization 10x from 5 to 50. Inna enjoys building diverse engineering organizations, with common vision, growth strategy and inclusive culture. Inna spent the last 6 years in Google Mountain View, and recently relocated back to Israel to lead GCP Database Migration Service.

Inna Weiner is a senior technical leader with 20+ years of global experience. A big data expert, specializing in data processing, storage, access, insider risk, privacy and policy compliance. During her 15 years at Google, Inna led engineering teams in consumer and enterprise products including Google Sites, Google Search and Google Analytics. Most recently she led the Google Search Analytics Data Warehouse, growing her organization 10x from 5 to 50. Inna enjoys building diverse engineering organizations, with common vision, growth strategy and inclusive culture. Inna spent the last 6 years in Google Mountain View, and recently relocated back to Israel to lead GCP Database Migration Service.

Meskin | English
11:10-11:40

Vector Similarity Server, a Short Intro

Vector similarity searches are a new type of DB, applicable for many use cases such as anomaly detection, recommendation and search.
In this talk, we would demonstrate building personalizes search engine with Vecsim and CLIP.

Uri Goren

CEO, Argmax

Uri is currently leading Argmax ML, a machine learning consultancy. Uri is a Natural language processing and recommendation system expert. Programming since elementary school, having worked for several Fortune 500 companies, startups and the academia. Co-hosting ExplAInable with Tamir Nave, a machine learning podcast in Hebrew.

Uri is currently leading Argmax ML, a machine learning consultancy. Uri is a Natural language processing and recommendation system expert. Programming since elementary school, having worked for several Fortune 500 companies, startups and the academia. Co-hosting ExplAInable with Tamir Nave, a machine learning podcast in Hebrew.

Rovina | Hebrew
11:45-12:00

Death by Thousand Schema Changes: The Mechanics of Schema Evolution

Schema changes should be a simple everyday event right?
They Are Not!

Even with years of experience, production get’s broken more often than we’d like to admit when our schema evolves, and in this talk, we’re going to explore why.

Through the analysis of several production and data incidents, we’re going to uncover the mechanics of schema changes, their symbiosis with production environments, and uncover the overwhelming complexity of modern software systems.

You’re going to leave this talk with a concrete model on how to address schema changes methodically. Hopefully making your next one not as painful as mine.

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Boris Cherkasky

Software Engineer, Finout

A software engineer with passion (some say obsession) for observability, charts, and dashboards. In the last ten years, did anything from low-level safety-critical control logic to high availability cloud applications, and currently helping get the Finops gospel to the masses as an engineer at Finout. Amateur tech blogger, mediocre cook with a slight Scuba diving addiction.

A software engineer with passion (some say obsession) for observability, charts, and dashboards. In the last ten years, did anything from low-level safety-critical control logic to high availability cloud applications, and currently helping get the Finops gospel to the masses as an engineer at Finout. Amateur tech blogger, mediocre cook with a slight Scuba diving addiction.

Meskin | Hebrew
11:45-12:00

Divide and Conquer in the Works: Leveraging Sub-Population Splits for Accurate Fraud Predictive Models

In the fraud prevention industry, identifying fraudulent activities can be a complex and challenging task. Accurate predictive models are crucial in order to prevent fraudulent activities before they occur. Splitting to sub-population is a powerful technique that can improve the accuracy of predictive models by segmenting data into meaningful groups based on common characteristics.

In this session, we will explore the fundamental principles of data splitting and its practical application in real world scenarios. We will discuss the benefits of data segmentation in fraud prevention and develop a comprehensive understanding of how it can help refine predictive models, minimize false positives, and optimize business value in the industry.
Furthermore, we will learn about the best practices for executing data splitting strategies and how to seamlessly integrate these strategies into existing fraud prevention systems.

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Tal Laron

Data Science Team Lead, Riskified

Tal Laron is a Data Science Team Lead at Riskified, responsible for Riskified’s models and DS components. Tal has extensive data science experience across multiple fields, and, before Riskified, she worked in several startups. Tal holds a B.Sc in statistics and economics from the Tel Aviv University and a master’s degree in financial economics, a global program from the Reichman University. As a former gymnast she enjoys sports and sporting events, beside her great love for data and sports, she is a mother of two, Assaf & Noa.

Tal Laron is a Data Science Team Lead at Riskified, responsible for Riskified’s models and DS components. Tal has extensive data science experience across multiple fields, and, before Riskified, she worked in several startups. Tal holds a B.Sc in statistics and economics from the Tel Aviv University and a master’s degree in financial economics, a global program from the Reichman University. As a former gymnast she enjoys sports and sporting events, beside her great love for data and sports, she is a mother of two, Assaf & Noa.

Rovina | Hebrew
12:05-12:35

Cool Down Your Compute: Advanced Iceberg Features That Will Help You Manage Your Data

Apache Iceberg gave rise to many important features that free data engineers from many common pains, such as schema evolution, concurrent writes and reads, and scan performance. However, the Iceberg project is much more than a spec. It rides on a Java library that exposes a powerful API, and on a set of metadata tables that enable building powerful applications.
The object of this talk is to highlight more nuanced yet important features in Iceberg API, and demonstrate the capabilities they make available for us. Specifically, I will focus on meta-data queries, tagging/branching, and how to run data management tasks.

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Alon Agmon

Engineering Group Lead, AppsFlyer

Senior engineering manager. Leading the Biz Data & Dev group @ AppsFlyer. Have been managing data engineering and data science teams over the past few years, and handling exceptions for more than a decade. Intrigued by functional programming, distributed systems, and the big data ecosystem. Fearless adopter of cutting edge data technologies.

Senior engineering manager. Leading the Biz Data & Dev group @ AppsFlyer. Have been managing data engineering and data science teams over the past few years, and handling exceptions for more than a decade. Intrigued by functional programming, distributed systems, and the big data ecosystem. Fearless adopter of cutting edge data technologies.

Meskin | Hebrew
12:05-12:35

How I Became Famous With Data Analysis, and How I Could Do It Much Better

Just a few months ago, I created a simple tableau dashboard with a nice dataset of all Israeli names over the years. The dashboard became viral, with almost 80,000 views, articles and interviews, which I could never predict.

In this lecture I’ll share the story behind the scenes. I’ll give my take on why it was so successful, and use this dashboard to illustrate all the mistakes I’ve made, and how I could do it much better.

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Efrat Garber Aran

Product Data Scientist, AI21 Labs

Efrat has a BA and MA on Hebrew literature from BGU, But after a short career in the fields of NGO’s and Renewal Judaism, she successfully Retrained and became a fraud analyst at Paypal. After 3 years there she moved to Lightricks, and after additional 3 years she started to work at AI21 Labs and there she is still working as a senior product data scientist in the Studio team. Efrat also performs as a spoken word poet.

Efrat has a BA and MA on Hebrew literature from BGU, But after a short career in the fields of NGO’s and Renewal Judaism, she successfully Retrained and became a fraud analyst at Paypal. After 3 years there she moved to Lightricks, and after additional 3 years she started to work at AI21 Labs and there she is still working as a senior product data scientist in the Studio team. Efrat also performs as a spoken word poet.

Rovina | Hebrew and English
12:40-13:10

Lightning talks

Scalable Seasonality In Five Minutes
English | Tal N. Mizrachi

In my talk, I will discuss a scalable seasonality data science project that I recently completed. The project aimed to identify and analyze seasonality patterns in large datasets, with a focus on developing a system that could handle a high volume of data.

To achieve this, I utilized a combination of traditional time series analysis techniques and modern machine learning algorithms. I also implemented parallel processing and distributed computing techniques to ensure that the system could handle the large amount of data in a timely and efficient manner.

I will also discuss the results and insights gained from the project, including examples of how the system was used to uncover previously unknown seasonality patterns in the data. Additionally, I will talk about the potential applications of the system in various industries and the future work that can be done to improve it.

 

Mapping the Way: Enhancing Map Validation with Python-Based Visualization
English | Naomi Weiser

Mobileye’s autonomous vehicle technology relies on special High Definition Maps to navigate, which are created from real-time crowdsourced data. For testing purposes, the maps are also created using processed video data as ground truth. Completing the long and costly map creation process with this video data revealed gaps or areas with insufficient coverage in the maps.

Learn how we solved this challenge and improved our process by developing a simple python-based tool that processes geo-referenced video data and extracts the GPS coordinates for display on an interactive HTML map using python libraries, helping data collection engineers visualize map coverage, identify areas that require further data collection and generate comprehensive reports.

 

Is There Money in Moneyball?
Hebrew | Asaf Shapira

The movie Moneyball (w/Brad pitt) spread the idea of game analytics to the general public and made it a topic for popular discourse.
The concept of game analytics sounds great and the story of the Oakland Athletics (the heroes of the movie) – as seen in the movie – is a great story.
But is it also a TRUE story?
By comparing the Oakland team’s data to other teams we can see that Moneyball is a bit hyped. Makes you wonder it other data analysis “myths” are hyped too…
So, what can we learn from it?
For starters – calibrate our expectations, keep adjusting our strategies and learn how to market our analysis.

 

Three Ways to Better Leverage Your Data Manager
Hebrew | Danielle Menuhin

Everybody knows you can’t have ML, DL or AI without tagged data. But who’s tagging all that data and managing this operation?
That would be us, your friendly data operations people. From general assistants to the lone researcher working on an AI/DL/ML model, we have evolved into a full-on profession with our own methodologies and best practices.
In light of recent development it becomes clear, and even OpenAI CTO Greg Brockeman recently tweeted it, that manual data work is the most important part of any machine learning operation, so you’re invited to hear more about the people who look your data directly in the eye and learn how to better leverage what they know.
This talk will include insights from my extensive experience as data operations manager, as well as from all the collective experiences of data operations professionals from our newly emerging community.

 

Beyond Numbers: Insights About Data Storytelling From 100+ Presentations
Hebrew | Roee Ben David

Data professionals are expected to be storyteller wizards, creating and presenting countless presentations to share insights and discoveries.

Over the past three years, I have presented over 100 decks as a data analyst. At that time, I made a lot of mistakes, while learning and applying some tricks to become a better storyteller.

My talk will share six tips that have worked for me and might work for you too.

 

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Tal N. Mizrachi - Analysis Paralysis

Data Academic Lead, Masterschool

Hey! I’m Tal Mizrachi, AKA Analysis Paralysis. I’m a Data scientist, Educator and Mentor trying to make data science, analytical methods and programming accessible and fun to learn about. I’m married to Adi, we have 2 daughters and a big black dog.

Hey! I’m Tal Mizrachi, AKA Analysis Paralysis. I’m a Data scientist, Educator and Mentor trying to make data science, analytical methods and programming accessible and fun to learn about. I’m married to Adi, we have 2 daughters and a big black dog.

Naomi Weiser

Data & Operations Manager, Mobileye

Naomi Weiser is a Data & Operations Manager at Mobileye, where she leads the development of data and automation services. With over 25 years of experience in software development and DevOps, Naomi is an accomplished Full Stack developer and specializes in the creation of automated systems. Naomi is known for her excellent problem-solving skills, creative thinking, and technical leadership. She is dedicated to establishing, enhancing, and supporting automated systems to improve customer satisfaction and drive overall operational improvements.

Naomi Weiser is a Data & Operations Manager at Mobileye, where she leads the development of data and automation services. With over 25 years of experience in software development and DevOps, Naomi is an accomplished Full Stack developer and specializes in the creation of automated systems. Naomi is known for her excellent problem-solving skills, creative thinking, and technical leadership. She is dedicated to establishing, enhancing, and supporting automated systems to improve customer satisfaction and drive overall operational improvements.

Asaf Shapira

Network Analyst, NETfrix

Major (Ret.). I served many years in military intelligence (8200 & more). Network Science analyst (Ind.). The podcaster of NETfrix - The Network Science Podcast (Hebrew & English versions).

Major (Ret.). I served many years in military intelligence (8200 & more). Network Science analyst (Ind.). The podcaster of NETfrix - The Network Science Podcast (Hebrew & English versions).

Danielle Menuhin

Data Operations Manager, eBay

Like most data operations professionals, my career started accidentally - Following a BA in Linguistics and Gender Studies and work in 2 startups I found myself at eBay’s taxonomy team, only to move later on to the catalog data operations group where I am today. After finding out I actually do have a career, I founded the Data Operations IL community this last summer, where we started having meetups and long over-due larger scale professional conversations. In between those roles and activities, I’m a fully fledged coffee snob, current 3rd place in the Israeli Aeropress Coffee Brewing Championship, and a home coffee roaster.

Like most data operations professionals, my career started accidentally - Following a BA in Linguistics and Gender Studies and work in 2 startups I found myself at eBay’s taxonomy team, only to move later on to the catalog data operations group where I am today. After finding out I actually do have a career, I founded the Data Operations IL community this last summer, where we started having meetups and long over-due larger scale professional conversations. In between those roles and activities, I’m a fully fledged coffee snob, current 3rd place in the Israeli Aeropress Coffee Brewing Championship, and a home coffee roaster.

Roee Ben David

Senior Growth Product Data Analyst, Honeybook

Roee Ben David is a Senior Data Analyst at Honeybook with a passion for using data to drive growth. With over 7 years of experience as a product and business analyst, Roee has honed his skills in analyzing data to identify key trends and patterns. He has been a member of Honeybook's growth team for the past three years, working to expand the company’s member base. Roee's expertise includes managing the AB test framework, as well as using storytelling as a way to make data more engaging, actionable and accessible to everyone.

Roee Ben David is a Senior Data Analyst at Honeybook with a passion for using data to drive growth. With over 7 years of experience as a product and business analyst, Roee has honed his skills in analyzing data to identify key trends and patterns. He has been a member of Honeybook's growth team for the past three years, working to expand the company’s member base. Roee's expertise includes managing the AB test framework, as well as using storytelling as a way to make data more engaging, actionable and accessible to everyone.

Meskin | English
12:40-12:55

A Case of Customized Clustering: Choose Your Loss

Plenty of sophisticated out-of-the-box clustering solutions are readily available in data science libraries. However, they are of little help when the clusters must optimize a complex and specific loss function that is not easily differentiable. In this talk, I will walk through a simple tailored clustering algorithm we developed, which uses a customized plug-and-play loss function to cluster sequential data. By utilizing the additivity of our loss function, we dynamically optimize it in polynomial time. This approach is applicable to a vast range of single dimensional clustering problems, from time based traffic modeling, to age based insurance pricing!

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Maya Samuels

Data Scientist, Via

Maya Samuels is a data scientist at Via Transportation, a top software provider in the shared transportation industry, with over 500 services worldwide. As a member of the algo-routing group, her work focuses on building, improving and analyzing state-of-the-art traffic models to ensure dependable, efficient and cost-effective mobility solutions. Maya holds a B.Sc. in Physics and Chemistry and a M.Sc. in Atmospheric Science, both from the Hebrew University of Jerusalem.

Maya Samuels is a data scientist at Via Transportation, a top software provider in the shared transportation industry, with over 500 services worldwide. As a member of the algo-routing group, her work focuses on building, improving and analyzing state-of-the-art traffic models to ensure dependable, efficient and cost-effective mobility solutions. Maya holds a B.Sc. in Physics and Chemistry and a M.Sc. in Atmospheric Science, both from the Hebrew University of Jerusalem.

13:10-14:00

Lunch

Rovina | Hebrew
14:00-14:30

Help Your Organizations to Make Great Decisions Quickly by Providing High-Fidelity Data

We live in an era of big data and machine learning, where data drives the decision-making processes for organizations. This data must be managed and processed effectively to ensure the success of any system.

A healthy data journey is a foundation for any big data and machine learning-based system to reach its full potential. The data journey encompasses all aspects of data management, from its collection and validation to its transformation into insights that inform decisions.

The importance of a healthy data journey cannot be overstated. Without it, big data and machine learning systems results will be inaccurate, unreliable, and ultimately, less valuable.

Today, we will delve into the critical components of a healthy data journey and the steps organizations can take to ensure the quality of their data. We will explore the role of input validation, API design, data health monitoring, and well-designed data transformation mechanisms in ensuring the success of a big data and machine learning-based system.

By the end of this talk, you will have a deeper understanding of the significance of a healthy data journey and the steps you can take to ensure that your big data and machine learning-based systems are functioning at their best. So, join us as we embark on this journey to discover the key to unlocking the full potential of big data and machine learning.

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Alik Berezovsky

System Architect, Riskified

Highly experienced System Architect in the Fraud Detection industry. With a strong background in both management and technical expertise As a seasoned engineering manager, Alik has a proven track record of leading development teams from requirement analysis through to delivery. With in-depth knowledge of cloud solutions, microservices, big data and continuous deployment, Alik is well-equipped to drive innovation and ensure the successful completion of projects. In addition to professional achievements, Alik is also an individual who values personal growth and challenge. Alik has a passion for learning new skills and taking on new experiences, such as landscape painting and extreme sports. With a dedication to both personal and professional growth, Alik is a driven and dynamic individual who consistently strives to achieve excellence. Whether leading teams or developing cutting-edge solutions.

Highly experienced System Architect in the Fraud Detection industry. With a strong background in both management and technical expertise As a seasoned engineering manager, Alik has a proven track record of leading development teams from requirement analysis through to delivery. With in-depth knowledge of cloud solutions, microservices, big data and continuous deployment, Alik is well-equipped to drive innovation and ensure the successful completion of projects. In addition to professional achievements, Alik is also an individual who values personal growth and challenge. Alik has a passion for learning new skills and taking on new experiences, such as landscape painting and extreme sports. With a dedication to both personal and professional growth, Alik is a driven and dynamic individual who consistently strives to achieve excellence. Whether leading teams or developing cutting-edge solutions.

Meskin | Hebrew
14:00-14:30

Are You Sure That You’re Sure? Estimating Confidence in Your Data Work

Trust is hard to gain and easy to lose, especially when it comes to data. Recall the last time you sent out a report: were your hands a little sweaty? Did your heart skip a beat? This is because data is never 100% accurate. As data people we strive for that sweet spot that’s between data-integrity-risk and that unattainable perfectly-correct-and-complete-data. We call that spot “good enough”. But can you be truly confident that your output is “good enough”? I assert that you definitely can! In this talk I’ll share my data confidence meter – a framework for increasing assurance in your work and distilling the trust in the stakeholders you’re working with.

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Tali Fulman

Head of Data Guild, Simply

Tali is Head of Data Guild at Simply (formerly Joytunes). She has over 10 years in the fields of data, first as a hands-on analyst and later as senior management. As one of the leading forces in the Israeli data community, Tali co-founded Data Queens, a non-profit Israeli community for women in the fields of data which has over 1300 members. Her community involvement includes presenting at events, teaching in courses and producing meetups and conferences. Tali holds a degree in Statistics from Tel Aviv University.

Tali is Head of Data Guild at Simply (formerly Joytunes). She has over 10 years in the fields of data, first as a hands-on analyst and later as senior management. As one of the leading forces in the Israeli data community, Tali co-founded Data Queens, a non-profit Israeli community for women in the fields of data which has over 1300 members. Her community involvement includes presenting at events, teaching in courses and producing meetups and conferences. Tali holds a degree in Statistics from Tel Aviv University.

Rovina | Hebrew
14:35-14:50

Real Time and Batch in a Single Dataset

Traditionally, RealTime and Batch data pipelines are consumed from different sources.
At Outbrain we’ve built a unified infrastructure for batch & RT processing, that enables us to stream all data into a single dataset.
In this session, I will explain how realtime data is unified with batch calculations, to produce a coherent view of our core data.

Urit Greifman

Senior Data Engineer, Outbrain

Currently senior data engineer, performed as a team leader. Graduated Ms.c. of Information Systems Management.

Currently senior data engineer, performed as a team leader. Graduated Ms.c. of Information Systems Management.

Meskin | Hebrew
14:35-14:50

Selling the Future: Navigating the Non-Tech Challenges of ML Products

In this talk, we will delve into the often overlooked but critical challenges of bringing a machine learning product to market. From idea conception to product launch, there are numerous non-technical hurdles that must be overcome in order to create and sell a successful machine learning product.

We will talk about what our product, dev teams and business teams need change or be aware of for our ML product to succeed.
Join us as we explore the obstacles, lessons learned, and best practices in this field. We will discuss key areas such as product validation, go-to-market strategy, and stakeholder management.

This talk is aimed at data scientists, machine learning engineers, and business professionals who work or think of working on machine learning products. Whether you are starting a new venture or seeking to improve the success of an existing product, I hope this session will provide valuable insights and inspiration for your journey.

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Elena Levi

Director of Product, AppsFlyer

Elena is a data-driven Director of Product at AppsFlyer, and over the past few years, she's been focused on machine learning products. She began her career as a Product Manager in the Israeli Intelligence Corps, and continued as a data analyst in the private sector, which has since made her data-obsessed. She's gained extensive experience in managing BI teams, working in data engineering, and creating products for data driven people.

Elena is a data-driven Director of Product at AppsFlyer, and over the past few years, she's been focused on machine learning products. She began her career as a Product Manager in the Israeli Intelligence Corps, and continued as a data analyst in the private sector, which has since made her data-obsessed. She's gained extensive experience in managing BI teams, working in data engineering, and creating products for data driven people.

Rovina | Hebrew
14:55-15:25

Dismantling Big Data With DuckDB

What if I told you, you do not need all of your Big Data Architecture and Tech Stack. What if I told you, you could could save a lot of money and resources all the while improving developer experience for all your data needs?

DuckDB is revolutionizing the way we view and handle Big Data. I will show you how you can utilize DuckDB to your advantage and address your data needs using this in-process in-memory OLAP DB in ways you never thought possible.

Having worked with Big Data and OLAP engines for many years now I know exactly where this new OLAP engine would have the highest impact in your architecture and how you should apply it.

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Yoav Nordmann

Tech Lead & Architect, Tikal Knowledge

Hi, I’m Yoav Nordmann. I am a Backend Tech Lead and Architect for Distributed Systems and Data. I have over 20 years of experience having worked in numerous and diverse companies. I am passionate about new and emerging technologies, knowledge sharing and I am a fierce advocate for open source. I believe that anything can be achieved together as a team!

Hi, I’m Yoav Nordmann. I am a Backend Tech Lead and Architect for Distributed Systems and Data. I have over 20 years of experience having worked in numerous and diverse companies. I am passionate about new and emerging technologies, knowledge sharing and I am a fierce advocate for open source. I believe that anything can be achieved together as a team!

Meskin | English
14:55-15:25

Teaching Your Model to Do Two Things at Once

In the world of job search, we must carefully balance the needs of two parties: jobseekers and employers. Jobseekers expect highly relevant jobs to show up at the top of their search results while employers expect their position in search results to correlate well with their bids.
Correspondingly, in the past few years we’ve designed and redesigned job ranking algorithms which try to solve two problems at once. Solving the first problem serves our jobseekers: how can you optimally rank jobs given a search query and thereby ensure a positive user experience? Solving the second problem serves our employers: how can you accurately estimate the probability that a jobseeker will click on a job and thereby use this click-through-rate to determine business value?
Today, we’ll share how we’ve taught a model to do two things at once. We’ll explore past, current, and future solutions and review the exciting challenges along the way.

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Ritvik Kharkar

Data Scientist, ZipRecruiter

Ritvik is a Data Scientist at ZipRecruiter where he works on challenging problems in the Search space to bring a great experience to jobseekers. In his free time, he enjoys cooking and working on his data science focussed YouTube channel of 10+ years.

Ritvik is a Data Scientist at ZipRecruiter where he works on challenging problems in the Search space to bring a great experience to jobseekers. In his free time, he enjoys cooking and working on his data science focussed YouTube channel of 10+ years.

15:25-15:40

Coffee Break

Rovina | Hebrew
15:40-15:55

Is AI Ready to Fully Take on the Work of Humans? Depending on the Use Case

eBay regularly works with a gigantic pool of data, collaborating with many different vendors in the process. In the past few years, the industry has seen an ongoing surge in the trend of using AI to perform actions so far done by humans; and here at eBay, too, we’ve been continuously examining this alternative, among other things to automate our catalog content management processes.

But our attempts, well… how should we put it? Made us realize that to date, the very best results can be achieved by using a combination of Data Operations professionals and ML automations. Simply put, human-machine synergy.

Today, we know that a scenario where AI fully replaces humans is at least a few more years away.

So, how did we create our combined work method? What is the proper task division between humans and machines? How do we make sure the human input is being leveraged to enhance future machine output? And do we really need to worry about being replaced?
All this and more will be discussed as part of the talk.

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Liav Sharon

Senior Product Manager, eBay

Senior PM at ebay structured data group. Has worked in ebay for the past 8 years in different positions. Started as a data analyst, lead a team of operations and analytics and a PM for the past 5 years. Lead multiple solutions of applied machine learning data pipeline implementation for different data quality projects. Lives in Tel Aviv loves surfing and the sea.

Senior PM at ebay structured data group. Has worked in ebay for the past 8 years in different positions. Started as a data analyst, lead a team of operations and analytics and a PM for the past 5 years. Lead multiple solutions of applied machine learning data pipeline implementation for different data quality projects. Lives in Tel Aviv loves surfing and the sea.

Meskin | Hebrew
15:40-15:55

Testing Machine Learning Code and Artifacts, the Sane Way

Machine learning code is known for a lot of things, but testing is not one of those. Let’s face it testing machine learning code is challenging! We have been missing out on one of the biggest productivity boosters in modern software development. This talk will hopefully change it a bit

In this talk, rather than treat testing as a “necessary evil”, we will offer several testing strategies to make it easy and somewhat fun. We will cover a few simple but powerful tools for keeping your code problem-free.

Eyal Trabelsi

Data Architect, Bigabid

Enthusiastic Software Engineer with big passion for Python, Machine Learning, Databases Scale and Performance Optimisations and making all of these easy to use.

Enthusiastic Software Engineer with big passion for Python, Machine Learning, Databases Scale and Performance Optimisations and making all of these easy to use.

Rovina | Hebrew
16:00-16:30

Taking Your Cloud Vendor to the Next Level: Solving Massive-Scale Data Challenges

Akamai’s content delivery network (CDN) processes about 30% of the internet’s daily traffic, resulting in a massive amount of data that presents engineering challenges, both internally and with cloud vendors.
In this talk, we will discuss the barriers faced while building a data infrastructure on Azure, Databricks, and Kafka to meet strict SLAs, hitting the limits of some of our cloud vendors’ services.
We will describe the iterative process of re-architecting a massive-scale data platform using the aforementioned technologies.
We will also delve into how today, Akamai is able to quickly ingest and make available to customers TBs of data, as well as efficiently query PBs of data and return results within 10 seconds for most queries.
This discussion will provide valuable insights for attendees and organizations seeking to effectively process and analyze large amounts of data.

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Itai Yaffe

Senior Big Data Architect, Akamai

Itai Yaffe is a Senior Big Data Architect at Akamai. Prior to Akamai, Itai was a Senior Solutions Architect at Databricks, a Principal Solutions Architect at Imply, and a big data tech lead at Nielsen Identity, where he dealt with big data challenges using tools like Spark, Druid, Kafka, and others. He is also a part of the Israeli chapter's core team of Women in Big Data. Itai is keen on sharing his knowledge and has presented his real-life experience in various forums in the past.

Itai Yaffe is a Senior Big Data Architect at Akamai. Prior to Akamai, Itai was a Senior Solutions Architect at Databricks, a Principal Solutions Architect at Imply, and a big data tech lead at Nielsen Identity, where he dealt with big data challenges using tools like Spark, Druid, Kafka, and others. He is also a part of the Israeli chapter's core team of Women in Big Data. Itai is keen on sharing his knowledge and has presented his real-life experience in various forums in the past.

Tomer Patel

Engineering Manager, Akamai

Tomer currently works as Engineering Manager at Akamai Technologies, where he leads a group of Data engineers, Software developers and DevOps at scale. Previously Tomer worked as Team Lead at Clarizen (Now Planview).

Tomer currently works as Engineering Manager at Akamai Technologies, where he leads a group of Data engineers, Software developers and DevOps at scale. Previously Tomer worked as Team Lead at Clarizen (Now Planview).

Meskin | Hebrew
16:00-16:30

The Last Mile of Machine Learning Apps

AI apps are harder to succeed with than general software apps; you can have the best research, top developers and the sharpest product team in town but still face failures.
For many AI teams a too common script starts with getting a solid product requirement (like to predict customers at risk to churn), continues with developing a cutting-edge, super accurate model, but eventually once seeing almost no usage among customers, the journey ends with the app being pulled back due to low ROI, doom to failure. But this is not a prophecy!

Much can be done to make sure our apps will be useful. The last mile of users’ nurturing is where most AI apps fail; While developing a working model is the enabler for our solutions, it’s important not to forget that the end users are the ones to decide our apps’ success.

During the talk, we will walk through five key failure points to avoid. Hard learned lessons to make sure your AI apps will make it to the finish line.

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Ori Abramovsky

Head of Data Science, SpectralOps (a CheckPoint company)

Ori Abramovsky is the Head of Data Science at SpectralOps, a CheckPoint company, where he leads the development and application of machine learning models to the source code domain. With extensive experience in various machine learning types and paradigms, Ori specializes in bringing AI applications to life. He is committed to bridging the gap between theory and real-world application, and is passionate about harnessing the power of AI to solve complex business challenges. Whether he is working on cutting-edge research or collaborating with cross-functional teams, Ori's expertise spans the entire spectrum of AI development, from ideation to implementation.

Ori Abramovsky is the Head of Data Science at SpectralOps, a CheckPoint company, where he leads the development and application of machine learning models to the source code domain. With extensive experience in various machine learning types and paradigms, Ori specializes in bringing AI applications to life. He is committed to bridging the gap between theory and real-world application, and is passionate about harnessing the power of AI to solve complex business challenges. Whether he is working on cutting-edge research or collaborating with cross-functional teams, Ori's expertise spans the entire spectrum of AI development, from ideation to implementation.

Rovina | Hebrew
16:35-17:00

Keynote - When Personalization Meets Creativity: How Personalizing the User Journey Boosts Creativity and Optimizes Revenue

As data professionals, we always aim to leverage data which then enables users to benefit from the endless possibilities of our applications and services. In this talk, we will share how we built personalized multimodal machine learning models based on our data and machine learning pipelines to personalize Lightricks users’ journey so they can boost their creativity and drive business success. By the end of the talk, you’ll learn best practices which you can adopt to boost your own business goals through personalization.

Dr. Asi Messica

VP Data Science, Lightricks

Dr. Asi Messica is VP Data Science at Lightricks and a lecturer at Reichman University. Before joining Lightricks, she held various data science, product, and development management positions at Fiverr, SAP, RSA, and more. She pursued her Ph.D. in the area of Recommender Systems at Ben-Gurion University. Her professional interests include machine learning, personalization, information retrieval, NLP, and reinforcement learning.

Dr. Asi Messica is VP Data Science at Lightricks and a lecturer at Reichman University. Before joining Lightricks, she held various data science, product, and development management positions at Fiverr, SAP, RSA, and more. She pursued her Ph.D. in the area of Recommender Systems at Ben-Gurion University. Her professional interests include machine learning, personalization, information retrieval, NLP, and reinforcement learning.