Agenda

08:30-09:30

Registration & Breakfast

Rovina | Hebrew
09:30-09:50

Opening session

Rovina | Hebrew
09:50-10:15

Keynote – Building ML-Driven Products for Business Users

The Gong product is used by business users (customer-facing professionals) on a daily basis. In this talk, Eilon will review some of the learnings around building products that are based on data and machine learning: what data-driven functionality is accepted well, and how to internally define and build such functionality. Eilon will provide examples from Gong’s lifecycle on areas that worked well and areas that didn’t.

#DataEngineering #DataScience #MLOps #Product

Eilon Reshef

Co-Founder and Chief Product Officer, Gong.io

Eilon Reshef is a co-founder and Chief Product Officer at Gong, the leading revenue intelligence platform, unlocking reality to help people and companies reach their full potential. Before co-founding Gong, Eilon co-founded Webcollage, a SaaS solution in the e-commerce infrastructure space. Eilon holds an M.Sc. and B.Sc. in Computer Science (summa cum laude) from the Weizmann Institute of Science and the Technion.

Eilon Reshef is a co-founder and Chief Product Officer at Gong, the leading revenue intelligence platform, unlocking reality to help people and companies reach their full potential. Before co-founding Gong, Eilon co-founded Webcollage, a SaaS solution in the e-commerce infrastructure space. Eilon holds an M.Sc. and B.Sc. in Computer Science (summa cum laude) from the Weizmann Institute of Science and the Technion.

Rovina | English
10:20-10:50

Keynote – The Data Manageability Revolution – How Data Trust Is Becoming the New North Star

Working with Data is hard. Intrinsic difficulties that we used to cope with by manual workarounds become massive manageability problems when data is big, diverse, and many of us are working it in parallel. In this talk we will review the evolution of questions such as: What data do I have and where is it? How do I ensure high quality data? How do I cope with my data being transient? and see how the answers to those questions evolved into new categories of data tools that become a standard in every data architecture.

#DataEngineering

Einat Orr, PhD

Co-Founder and CEO, Treeverse

Einat Orr is the co-founder and CEO of Treeverse, the company behind lakeFS, an open-source platform that delivers a Git-like experience to object-storage-based data lakes. Einat previously led several engineering organizations, most recently as the CTO of SimilarWeb. She holds a PhD in mathematics in the field of optimization in graph theory from Tel Aviv University.

Einat Orr is the co-founder and CEO of Treeverse, the company behind lakeFS, an open-source platform that delivers a Git-like experience to object-storage-based data lakes. Einat previously led several engineering organizations, most recently as the CTO of SimilarWeb. She holds a PhD in mathematics in the field of optimization in graph theory from Tel Aviv University.

10:50-11:10

Coffee Break

Rovina | English
11:10-11:40

Everything You Always Wanted to Know About Data Mesh* (*But Were Afraid to Ask)

Data Mesh is the new buzzword in the data management world. It describes a distributed approach to managing your central data lake and promises to become “the microservices of data lakes.”

In this talk, Erez will explain why you should care about this new buzz, walk you through its core concepts, share his personal experience from implementing it, and tell you what to watch out for (Not necessarily in this order).

#DataEngineering

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Erez Lotan

VP R&D Platform, Skai

Reversim speaker since 2016. One time Reversim Moderator. Developer, Engineering Manager, Architect, Techie.

Reversim speaker since 2016. One time Reversim Moderator. Developer, Engineering Manager, Architect, Techie.

Meskin | English
11:10-11:40

Reaching the Top – Can We Train ML Models Which Are Both Accurate and Fair?

We usually optimize ML models for metrics like accuracy or precision. But can we really tell if this optimization leads us to the best solution for the problem ? how can we really define what is the best model for a certain problem?

In this talk, we will see how ML models that may seem optimal, can create discrimination.
We will review common notions of fairness and show why it’s hard to even agree on what is objectively fair.
We will propose a new notion of fairness, named ‘consistency score’, which is subjective to the problem at hand and will show how to select the top model, which is optimized both for accuracy as well as consistency.
We will also share a python package – the bias detector – that can help any data scientist detect bias in the ML models they develop.

#DataScience #Product

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Shir Meir Lador

Data Science Group Manager, Intuit

Shir Meir Lador is a Data Science group manager at Intuit, a global leader in the industry of financial management software. Additionally, Shir is the co-founder of PyData Tel Aviv meetups, WiDS Tel Aviv ambassador, the co-host of “Unsupervised” (a podcast discussing data science in Israel), and gives talks at various machine learning and data science conferences and meetups. Shir holds an M.Sc. in electrical engineering and computers with a major in machine learning and signal processing from Ben-Gurion University.

Shir Meir Lador is a Data Science group manager at Intuit, a global leader in the industry of financial management software. Additionally, Shir is the co-founder of PyData Tel Aviv meetups, WiDS Tel Aviv ambassador, the co-host of “Unsupervised” (a podcast discussing data science in Israel), and gives talks at various machine learning and data science conferences and meetups. Shir holds an M.Sc. in electrical engineering and computers with a major in machine learning and signal processing from Ben-Gurion University.

Rovina | English
11:45-12:15

The Evolution of Meta’s Batch Pipelines Framework

In this talk we’ll see how Meta’s internal Batch Pipelines framework is developed according to DEs needs and feedback, resulting in a fully automated and privacy-aware data pipelines
Meta’s large scale enables the company to develop internal frameworks for DEs, focusing on problems unique to the company’s world of content.

In this joint DE-DI talk, we’ll show how the company’s unique structure enabled us to improve our Batch Pipelines Framework.
We’ll first give a birds eye overview of Meta’s Batch Pipelines tooling. We’ll then present the problems DEs face on a daily basis by using a fictional use case, highlighting the problems in the simple manual solutions.

We’ll iterate over the problems, improving the framework based on users feedback, up to a level that enables us to tackle complex issues such as managed schemas, automatic privacy management, rich types and unavoidable human errors.

#DataEngineering #BI 

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Sagi Goldshtein

Data Engineer, Meta

Sagi is a Data Engineer with a passion for finding simple solutions to complex problems. Sagi is part of Meta’s Central Growth DE team which is responsible for Meta’s growth accros the familiy of apps. He loves his job and most enjoys Meta’s culture where no one dares to have an opinion without backing it with data.

Sagi is a Data Engineer with a passion for finding simple solutions to complex problems. Sagi is part of Meta’s Central Growth DE team which is responsible for Meta’s growth accros the familiy of apps. He loves his job and most enjoys Meta’s culture where no one dares to have an opinion without backing it with data.

Ruty Mundel

Senior Software Engineer, Meta

Ruty is a Senior Software Engineer at Meta, working on Meta’s internal Batch Pipelines framework. She is data-driven, interested in complicated, large-scale problems, coming to Meta after over 4 years in Google, where she improved the web as part of the Search Console team. Writing her first line of code in high school, she immediately got hooked, all the way to doing her M.Sc. in Computer Science with a thesis focused on modeling ride sharing markets with tolls to decrease road congestion.

Ruty is a Senior Software Engineer at Meta, working on Meta’s internal Batch Pipelines framework. She is data-driven, interested in complicated, large-scale problems, coming to Meta after over 4 years in Google, where she improved the web as part of the Search Console team. Writing her first line of code in high school, she immediately got hooked, all the way to doing her M.Sc. in Computer Science with a thesis focused on modeling ride sharing markets with tolls to decrease road congestion.

Meskin | English
11:45-12:00

For My Next Trick: A Complex AI From Nothing!

It’s a Data Scientist’s dream: building a functional, complex AI model, with almost no data. Sadly, since a machine learning model’s complexity usually goes hand-in-hand with huge amounts of data, it’s a dream that usually doesn’t come true.


However, when my teammates and I built our AI messaging recommendation system, we found a way around that limitation. We used the magic of semi-supervised learning and developed an algorithm to help us fuzzy label data and generate synthetic data as well. Though the very little data we had was very noisy, we succeeded in creating a working AI messaging recommendation system.


Join me along my journey where we take traditional ML techniques and pair them with out-of-the-box thinking to handle the limitations set by data-availability. Let me show you how we created something from nothing.

#DataScience #MLOps #Product

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Matityahu Sarafzadeh

Data Scientist Team Lead, Soluto

Matityahu is a Data Scientist Team Leader at Soluto with 6 years of experience in fields such as predictive analytics, cyber security, NLP, and computer vision. A public speaker with a strong passion for the world of AI and a curious self-learner. Grab me for a beer if you’d like to chat about basketball or some new Data Science techniques.

Matityahu is a Data Scientist Team Leader at Soluto with 6 years of experience in fields such as predictive analytics, cyber security, NLP, and computer vision. A public speaker with a strong passion for the world of AI and a curious self-learner. Grab me for a beer if you’d like to chat about basketball or some new Data Science techniques.

Meskin | Hebrew
12:00-12:15

If Data Is a Story, Then How Should We Analyze It?

What is the connection between literature and data analysis?
“The permanent entity is water“ said Thales, and literature researches will say that the permanent entity is text – including data.
My professional track wasn’t standard – after a B.A. and M.A. in Hebrew literature, I went to work as a data analyst in high tech. Along the way I learned that there is, surprisingly, a link between the two fields.
In this lecture I’ll talk about this connection, by analyzing a data set and a poem together with you, with some literature analysis tools. I’ll show that translation, interpretation, and motifs from the world of literature can help to better understand the “text” – the big data.

#Analytics

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Efrat Garber Aran

Product Data Science, AI21 Labs

Efrat has a BA and MA in 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, and after that a Product Data Analyst at Lightricks. Now she is starting a new role as a Product Data Scientist at AI21 Labs.

Efrat has a BA and MA in 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, and after that a Product Data Analyst at Lightricks. Now she is starting a new role as a Product Data Scientist at AI21 Labs.

Rovina | Hebrew
12:20-12:50

Let’s Make Your CFO Happy; A Practical Guide for Cost Reduction

Take a look at your AWS bill, and you will probably find Hadoop, Spark, and Kafka at the top.

According to Gartner Forecasts, the worldwide end-user spending on public cloud services is forecast to grow by 23% in 2021, to a total of $332B. As organizations evolve and grow, data rates grow too, as do consequent cloud costs.

In this talk, we are going to address exactly this problem. We will understand what we are paying for, how to develop an economic mindset, where we can cut costs, and what we can proactively do to reduce our data infrastructure cost.

#DataEngineering

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Elad Leev

Data Engineer, Riskified

Elad Leev is a Data Engineer with strong experience managing complex production operations, with expertise in distributed systems and databases. Elad likes to solve complicated problems around delivering real-time infrastructure in perpetual growth. Elad is part of Riskified’s Data Streams team, which is responsible for the performance, scale, and stability of Riskified’s large-scale data streaming platform. Elad is active in the Kafka community by talking at tech conferences, writing blog posts, and contributing to open-source projects, and he got elected as Confluent Community Catalyst for the class of 2021.

Elad Leev is a Data Engineer with strong experience managing complex production operations, with expertise in distributed systems and databases. Elad likes to solve complicated problems around delivering real-time infrastructure in perpetual growth. Elad is part of Riskified’s Data Streams team, which is responsible for the performance, scale, and stability of Riskified’s large-scale data streaming platform. Elad is active in the Kafka community by talking at tech conferences, writing blog posts, and contributing to open-source projects, and he got elected as Confluent Community Catalyst for the class of 2021.

Meskin | Hebrew
12:20-12:50

Data Bias by Perception

The goal of this presentation is making you question the way you trust yourself around data and numbers. If following this talk you will pause before automatically jump to conclusions and take a deeper look I will consider it as a success.

I will take you through some fascinating examples including near sighted children, AB tests and Nicholas cage to demonstrate how even our basic perception of data and numbers might be misleading and can make us make the wrong decision.

#Analytics

Yigal Goldfine

VP Research & Algorithms, Pandologic

Vice President of Research and Algorithms working in the programmatic job advertising industry. Skilled in Data Science, Machine learning and algorithms both hands on and team management. Strong AI professional with two Bachelor of Science (BSc) degrees in Computer Science and Biotechnology engineering.

Vice President of Research and Algorithms working in the programmatic job advertising industry. Skilled in Data Science, Machine learning and algorithms both hands on and team management. Strong AI professional with two Bachelor of Science (BSc) degrees in Computer Science and Biotechnology engineering.

Rovina | Hebrew
12:55-13:10

Lightning Talks

Getting Work Done Using Task Forces: Examples and Practical Tips, by Moran Brody

Pushing new initiatives forward, beating the backlog and increasing collaboration are challenges every manager faces. If you tried tackling those while the day-to-day work and failed it is time to consider some out of the box workflows.
In this talk, I’ll talk about designated task forces and how you can use them to do things differently. I will share task force examples and practical tips from my previous experience as a team lead at Riskified. I hope that by showing you the challenges we were able to tackle you will be convinced to add task forces to your managerial toolkit.

 

3 Hiring Mistakes in Your Way to a Data Driven Culture, By Gil Adirim

Most modern organizations have immense amounts of data, but very few are actually data driven. Becoming data-driven is not impossible, and I can show you how to get there! In this lightning talk I’ll review the 3 most common hiring pitfalls that affect your data culture, and give you practical tools to start transforming your organization tomorrow!

 

History Always Repeats Itself – And So Do Histograms! By Gilit Saporta

In this short talk, I’d like to breeze through the most common types of histogram that any data researcher should apply when looking for anomalies.
Anomaly detection are the bread and butter of fraud prevention, so with just 3 examples (hourly/daily traffic breakdown, connection type breakdown, RSME visualization for device/OS), we can demonstrate the power of the histogram for every day analysis.

#DataEngineering #DataScience #BI #Analytics #Product

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Moran Brody

Data Scientist, Riskified

Moran is a Data Scientist with a passion for data analysis and exploration. She has been fighting fraud at Riskified for more than six years, four of them as a team leader. Recently returned to hands-on work after missing data so much. She believes the only way to commute while the week is cycling but an expert jeep driver on weekends. A black cats lover and a proud mom.

Moran is a Data Scientist with a passion for data analysis and exploration. She has been fighting fraud at Riskified for more than six years, four of them as a team leader. Recently returned to hands-on work after missing data so much. She believes the only way to commute while the week is cycling but an expert jeep driver on weekends. A black cats lover and a proud mom.

Gil Adirim

Head of Data, JoyTunes

In the biz since 2008, with a focus on leading data organizations since 2011. I’ve done quite a bit of 0-1 in small startups, and for the past 3.5 years have been doing 1-100 at JoyTunes as the head of a data guild which grew from just me to 22 (and continues to grow).

In the biz since 2008, with a focus on leading data organizations since 2011. I’ve done quite a bit of 0-1 in small startups, and for the past 3.5 years have been doing 1-100 at JoyTunes as the head of a data guild which grew from just me to 22 (and continues to grow).

Gilit Saporta

Director of Fraud Analytics, DoubleVerify

Gilit Saporta is director of fraud analytics at DoubleVerify and a fraud fighting veteran with over 2 decades of experience in fraud prevention and intelligence. She has led and trained fraud analysts at FraudSciences, PayPal, Forter, and Simplex. Gilit actively advocates for collaboration between research teams to keep fraudsters at bay. She’s cohost of Fraud Fighters IL meetups and chair of the in-person track of Cyber Week FraudCON conference.

Gilit Saporta is director of fraud analytics at DoubleVerify and a fraud fighting veteran with over 2 decades of experience in fraud prevention and intelligence. She has led and trained fraud analysts at FraudSciences, PayPal, Forter, and Simplex. Gilit actively advocates for collaboration between research teams to keep fraudsters at bay. She’s cohost of Fraud Fighters IL meetups and chair of the in-person track of Cyber Week FraudCON conference.

Meskin | English
12:55-13:10

Multi-Class Mathew’s Correlation Coefficient

The multi-class prediction had gained popularity over the recent years.
Thus measuring fit goodness becomes a cardinal question which researchers often has to deal with. There are several metrics that are commonly used for this task. However, when one has to decide about the right measurement, he must consider that different use-cases impose different constrains that govern this decision.
We suggest generalizing Mathew’s correlation coefficient into multi-dimensions. This generalization is based on geometrical interpretation of the generalized confusion matrix.

#DataScience

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Uri Itai, PhD

Senior Data Scientist, TRST AI

Mathematician, algorithm developer and a data scientist. With experience in these fields. PhD in applied math and years of experience in companies and in the machine learning community. Passionate in the linkage of it. From theoretical mathematics to programing issues and product and management aspects. So, if you share interest with me do not be a stranger.

Mathematician, algorithm developer and a data scientist. With experience in these fields. PhD in applied math and years of experience in companies and in the machine learning community. Passionate in the linkage of it. From theoretical mathematics to programing issues and product and management aspects. So, if you share interest with me do not be a stranger.

13:15-14:15

Lunch

Rovina | English
14:15-14:45

The Data Practitioners Guide to Metadata

Ever wonder about the secret behind the legendary data-driven cultures of companies like LinkedIn, Airbnb, and others? The answer is metadata!
In this session, Maggie Hays will walk you through emerging best practices for managing metadata across vast, disparate systems. You’ll hear about the common pitfalls that arise within rapidly evolving, fragmented data stacks and why it’s critical to prioritize metadata management early to get ahead of them. Maggie will share top lessons learned from the 3k strong DataHub Community, equipping you with practical next steps so you can begin wrangling your organization’s metadata.

#DataEngineering #DataScience #BI #Analytics #Product

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Maggie Hays

DataHub Community Product Manager, Acryl Data

Maggie Hays is the Community Product Manager for DataHub and part of the Founding Team at Acryl Data. She is passionate about building resources that allow data to be accessible, intuitive, and impactful for a wide spectrum of end-users so organizations can fully realize the power of data-backed decisions. Maggie is enthusiastic about providing opportunities for others to explore new technology, work collaboratively, and pursue life-long learning. You can find her regularly organizing data-focused hackathons, design sprints, mentoring programs, and more.

Maggie Hays is the Community Product Manager for DataHub and part of the Founding Team at Acryl Data. She is passionate about building resources that allow data to be accessible, intuitive, and impactful for a wide spectrum of end-users so organizations can fully realize the power of data-backed decisions. Maggie is enthusiastic about providing opportunities for others to explore new technology, work collaboratively, and pursue life-long learning. You can find her regularly organizing data-focused hackathons, design sprints, mentoring programs, and more.

Meskin | English
14:15-14:45

Correlating at Scale – Building Time-Series Clustering and Correlation Service for Big Data

Real-time similarity measurements can be challenging at a large scale in real-time. Usually, this problem is solved using approximation models calculated in advance (LSH-based) for finding suitable candidates during the serving phase.

We will present how Anodot uses LSH similarity approximation for large-scale time-series clustering and correlation, how Spark is used in our data pipelines, and explain the technical challenges of migrating from Hive to Spark.
Our initial time series clustering solution used AWS EMR service and Hive scripts to aggregate the data, extract feature vectors for each time series and calculate the LSH model signatures.

Later we discovered that Spark could significantly improve data processing performance. Moreover, this discovery enabled us to reduce the model calculation time and Data Lake size by supporting efficient compression methods. It gave our system the flexibility of using the same code base for SaaS and on-prem solutions.

#DataScience #Analytics

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Alexander Shereshevsky

ML Architect, Anodot

Machine Learning Architect, passionate about creating high-quality data products, massive data science topics, and scalable data pipelines. Over 15 years of experience with different data solutions development, data analysis, and data science projects. I experienced a wide range of machine learning products development, relational and NoSQL database platforms. I worked on different issues of MLOps and scalability, data system deployment on AWS/GCP, transactional systems, data warehousing, performance tuning, ETL processes, and data workflow management.

Machine Learning Architect, passionate about creating high-quality data products, massive data science topics, and scalable data pipelines. Over 15 years of experience with different data solutions development, data analysis, and data science projects. I experienced a wide range of machine learning products development, relational and NoSQL database platforms. I worked on different issues of MLOps and scalability, data system deployment on AWS/GCP, transactional systems, data warehousing, performance tuning, ETL processes, and data workflow management.

Rovina | English
14:50-15:20

Cardinality Control – From Batch to Stream

At AppsFlyer we deal with large volumes of data where some dimensions have very high cardinality — meaning many distinct values. We aggregate or data in order to provide interactive dashboards but for this aggregation to be effective we must carefully limit the cardinality of the input data.

I will show you how our approach to limiting cardinality has evolved from batch to a new streaming process that leverages mergeable probabilistic data structures.

#DataEngineering

Morris Feldman, PhD

Tech Lead, AppsFlyer

I researched epi-genetics as a post-doc at the Weizmann Institute in Israel after earning a PhD in Biophysics from University of California San Francisco. I left the world of academia to crack Big Data problems. That’s why I joined the AppsFlyer Dev team where I work with our Big Data using Spark through Clojure.

I researched epi-genetics as a post-doc at the Weizmann Institute in Israel after earning a PhD in Biophysics from University of California San Francisco. I left the world of academia to crack Big Data problems. That’s why I joined the AppsFlyer Dev team where I work with our Big Data using Spark through Clojure.

Meskin | Hebrew
14:50-15:05

Computer Vision for the Poor: How to Easily Reduce Deep Computer Vision to Shallow NLP

Building a task-specific image classification solution typically requires leveraging Computer Vision transfer learning techniques. It involves manipulating complex deep learning models, applying non trivial image preprocessing and using expensive hardware. But what if you could leverage existing image meta-data annotations to classify our images?

In this talk we will share a simple trick to make the task of building an image classifier as easy as building a standard text classifier. This reduction simplifies preprocessing and training and it also dramatically reduces the required hardware & computation time. This reduction is made possible by leveraging ready-made computer vision APIs provided by the public cloud vendors.

These APIs extract semantic textual labels from images that in turn can be used to build simple, shallow NLP classifiers. This simple reduction has helped us deliver fast & cheap Python-based image classification models to production and is widely used in Outbrain products.

#DataScience #Analytics

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Hila Weisman-Zohar

Data Science Guild Master, Outbrain

Hila has been processing, analyzing, and generating algorithms for the past decade. After earning her masters (summa cum laude) at BIU NLP lab and publishing at elite academic venues such as EMNLP, she began to research & develop algorithms that analyze call center calls as a senior researcher at NICE. She published 4 US patents and presented academic posters at various venues during that time. For the past two years, she has worked as DS Guild Master & algorithm engineer at Outbrain, where she works on large-scale super-fast algorithms for the native ads field. Hila also loves to teach and share her experience and has talked at various meetups and conferences.

Hila has been processing, analyzing, and generating algorithms for the past decade. After earning her masters (summa cum laude) at BIU NLP lab and publishing at elite academic venues such as EMNLP, she began to research & develop algorithms that analyze call center calls as a senior researcher at NICE. She published 4 US patents and presented academic posters at various venues during that time. For the past two years, she has worked as DS Guild Master & algorithm engineer at Outbrain, where she works on large-scale super-fast algorithms for the native ads field. Hila also loves to teach and share her experience and has talked at various meetups and conferences.

Assaf Klein

Data Science Group Manager, Outbrain

Assaf is an experienced, hands on, software and algorithms manger with unique multidisciplinary knowledge and proven leading skills. He as a wide experience in design and implementation of machine learning, recommender systems, NLP, data mining and optimization algorithms. He has been managing and building small-medium diverse engineering teams for over a decade and currently he is Recommendations Data Science Manager, leading a diverse group of algorithm engineers in charge of improving the key KPIs of Outbrain’s recommender system

Assaf is an experienced, hands on, software and algorithms manger with unique multidisciplinary knowledge and proven leading skills. He as a wide experience in design and implementation of machine learning, recommender systems, NLP, data mining and optimization algorithms. He has been managing and building small-medium diverse engineering teams for over a decade and currently he is Recommendations Data Science Manager, leading a diverse group of algorithm engineers in charge of improving the key KPIs of Outbrain’s recommender system

Meskin | Hebrew
15:05-15:20

Are There Any Benefits for Before and After Tests Over A/B Tests?

As analytics professionals, we will always favor A/B testing over before and after tests.
I want to challenge this a little bit and try to bring up to discussion some possible benefits to running before and after tests, from both an operational and an analytical point of view.

#Analytics

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Assaf Levinson

Head of Analytics, Stealth Mode Startup

Product and business oriented analytics professional with 10+ years of experience, 7 of them in building, leading and growing analytical teams and groups while working closely with product, R&D and business teams. Highly experienced with product analytics, business analytics, BI, AB tests (planning and analysis) and analytical work with data science teams and products.

Product and business oriented analytics professional with 10+ years of experience, 7 of them in building, leading and growing analytical teams and groups while working closely with product, R&D and business teams. Highly experienced with product analytics, business analytics, BI, AB tests (planning and analysis) and analytical work with data science teams and products.

15:20-15:35

Coffee Break

Rovina | Hebrew
15:35-16:05

Vespa or ClickHouse: What to Do After Elasticsearch

Elasticsearch is not dead. Yet.

Give it 5 more years, and it will no longer be the wide-spread technology that it is today.

I have been working with Elasticsearch since it’s dawn, started over 10 years ago with version 0.12 or something like that, and saw Elasticsearch becoming the de-facto standard technology for search, log analytics and real-time BI.

Today new technologies emerge and for some use-cases they might replace Elasticsearch completely. This session is about two of those technologies I consider the most prominent – ClickHouse and Vespa. Come to this talk to learn more 🙂

#DataEngineering

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Itamar Syn-Hershko

CTO and Founder, BigData Boutique

My passion is building innovative, scalable, and stable data platfoms for companies that make the world a better place. I spent the last decade or so building data platforms at scale, and then worked as a consultant and helped startups and large enterprises globally to do the same. Today as the CTO of BigData Boutique (https://bigdataboutique.com/) I lead a team of expert Big Data consultants and together we work hands-on with our customers to improve their journey in the data worlds, while leveraging a plethora of methodologies and technologies.

My passion is building innovative, scalable, and stable data platfoms for companies that make the world a better place. I spent the last decade or so building data platforms at scale, and then worked as a consultant and helped startups and large enterprises globally to do the same. Today as the CTO of BigData Boutique (https://bigdataboutique.com/) I lead a team of expert Big Data consultants and together we work hands-on with our customers to improve their journey in the data worlds, while leveraging a plethora of methodologies and technologies.

Meskin | Hebrew
15:35-16:05

Solving MLOps From First Principles

Selecting which tools to use in your workflow is one of the hardest challenges data teams face. Buyer’s remorse is real, and you continuously hear of new buzzwords you “just have to have in your stack”. In this talk, I’ll present a mental framework for thinking about MLOps challenges, and how to select the best tools for a task.

#DataEngineering #DataScience #MLOps

Guy Smoilovsky

Co-Founder and CTO, DagsHub

“Guy is a tech geek at heart and has spent most of his life working on various fields in tech – software, information security, data engineering, DevOps, and machine learning. He enjoys using this varied experience to find patterns, and bring new insights and useful tools to the world. Guy is currently the CTO & Co-Founder of DagsHub. It’s a platform that makes it easier for teams of data scientists and machine learning engineers to collaborate, especially on open source projects. DagsHub combines popular open-source tools and formats to version data, models, experiments, and code.”

“Guy is a tech geek at heart and has spent most of his life working on various fields in tech – software, information security, data engineering, DevOps, and machine learning. He enjoys using this varied experience to find patterns, and bring new insights and useful tools to the world. Guy is currently the CTO & Co-Founder of DagsHub. It’s a platform that makes it easier for teams of data scientists and machine learning engineers to collaborate, especially on open source projects. DagsHub combines popular open-source tools and formats to version data, models, experiments, and code.”

Rovina | Hebrew
16:10-16:25

Self Service: Getting Developers Out of Your Way

Do you feel you are the bottleneck of the development process? Drowning in maintaining fragile data pipelines? Wasting time on explaining data concepts to frontend engineers?

In this talk, we’ll review a few examples of how investing in making DataOps self-serviceable can help you get rid of the mundane work and focus on what really matters. As a long-time developer first-time data engineer at Yotpo, I’ll share with you how little customization can carry you a long way.

#DataEngineering #BI

Amir Halatzi

Senior Data Engineer, Yotpo

Amir Halatzi is a software craftsman and aspiring public speaker working at Yotpo. He’s been spending the last 10 years moving data from here to there, working in domains spanning from sports to commerce and communications. Proud father of 2, sci-fi geek, and prefers tabs over spaces.

Amir Halatzi is a software craftsman and aspiring public speaker working at Yotpo. He’s been spending the last 10 years moving data from here to there, working in domains spanning from sports to commerce and communications. Proud father of 2, sci-fi geek, and prefers tabs over spaces.

Meskin | Hebrew
16:10-16:25

Deploying Models in a Highly Regulated Industry

As Machine learning is growing in dominance in high stake situations (fintech, healthcare, autonomous driving) Model Risk Management will play an increasingly important role in years to come.


The financial industry has been using models to make high stake decisions for decades and have developed best practices for Model Risk Management.
The framework governs classical risks of poor performance, population drift and implementation mistakes, and also specific requirements for reproducibility, explainability and no discrimination toward protected classes.


How to responsibly manage hundreds of ML models, Some interlinked in various ways, in production?
How can you provide your users, decision makers and regulators with human understandable explanation of how a complex ML system makes decisions?
How can you make sure your system is not negatively impacting individuals based on factors like gender, ethnicity, and age?

#DataScience #MLOps #Product

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Doron Gurel

Head of Model Risk, Pagaya

Doron is a seasoned statistician and data scientist with experience in building data products bottom up at early-stage startups. For the last 3 years Doron has contributed to scaling Pagaya from 40 to 800+ employees by leading various Data Science and analytics teams throughout the model lifecycle from development through deployment and monitoring. At His current position Doron is heading the Model Risk group at Pagaya and helping to shape Pagaya’s AI product and offering for top tier banks.

Doron is a seasoned statistician and data scientist with experience in building data products bottom up at early-stage startups. For the last 3 years Doron has contributed to scaling Pagaya from 40 to 800+ employees by leading various Data Science and analytics teams throughout the model lifecycle from development through deployment and monitoring. At His current position Doron is heading the Model Risk group at Pagaya and helping to shape Pagaya’s AI product and offering for top tier banks.

Rovina | English
16:30-17:00

Keynote – Huge Language Models and Neuro-Symbolic AI

The term “neuro-symbolic AI” evokes heated debates, with neutal-net-diehards on one extreme, neuro-skeptics on the other, and the rest trying to have a rational conversation. We’ll have a rational conversation, in the context of natural language.

#DataScience

Prof. Yoav Shoham

Co-Founder, AI21 Labs

Yoav Shoham is professor emeritus of computer science at Stanford University. A leading AI expert, Prof. Shoham is Fellow of AAAI, ACM and the Game Theory Society. Among his awards are the IJCAI Research Excellence Award, the AAAI/ACM Allen Newell Award, and the ACM/SIGAI Autonomous Agents Research Award. His online Game Theory course has been watched by close to a million people. Prof. Shoham has founded several AI companies, including TradingDynamics (acquired by Ariba), Katango and Timeful (both acquired by Google), and AI21 Labs. Prof. Shoham also founded the AI Index initiative (www.AIindex.org), which tracks global AI activity and progress.

Yoav Shoham is professor emeritus of computer science at Stanford University. A leading AI expert, Prof. Shoham is Fellow of AAAI, ACM and the Game Theory Society. Among his awards are the IJCAI Research Excellence Award, the AAAI/ACM Allen Newell Award, and the ACM/SIGAI Autonomous Agents Research Award. His online Game Theory course has been watched by close to a million people. Prof. Shoham has founded several AI companies, including TradingDynamics (acquired by Ariba), Katango and Timeful (both acquired by Google), and AI21 Labs. Prof. Shoham also founded the AI Index initiative (www.AIindex.org), which tracks global AI activity and progress.