Name: Data lightning talks
Start: 2023-05-21T09:30:00+03:00
End: 2023-05-21
Location: ZipRecruiter Israel

Data lightning talks

ZipRecruiter

Experts in the field share their insights and experiences on a wide range of data-related topics

ZipRecruiter Israel

Data lightning talks are a must-attend event for data scientists, machine learning engineers, and data engineers. In this fast-paced and engaging event, experts in the field share their insights and experiences on a wide range of data-related topics, from the latest technologies and trends to practical applications in the real world. Whether you&#x27;re looking to expand your knowledge, network with peers, or simply stay up-to-date on the latest developments in the field, this event is the perfect opportunity to do so.0﻿9:30- Light breakfast &amp; Networking 1﻿0:00- First round (3 speakers TBA) 1﻿0:20- Q&amp;A1﻿0:30- Second Round (3 Speakers TBA) 1﻿0:50- Q&amp;A 1﻿1:15 - See you next timeT﻿his event will be held in English.S﻿speakers and talks: Jonatan Bobrutsky-Haim, Data Scientist at ZiprecruiterModeling the marketplace with graph neural networks. The employment marketplace can be modeled as a heterogeneous bipartite graph, where jobseekers and job listings are entities (nodes) and engagements on ZipRecruiter’s platform like click or apply on the jobseeker side, and thumbs-up/down on the employer side, form the edges connecting them together and forming the relations between them.Using GNNs allow for more flexible and convenient way to model multi-hop connectivity from user-item interactions, and have been demonstrated to be effective for recommendation.Mikey levinger, Data Scientist at Melio When ML models are in production, one often needs to keep tabs on the data drift. The goal is to detect changes in the input data distributions to make sure the model still operates in a familiar environment. Applying statistical tests to compare the new data with the old is one way to do it.I aim to build intuition on how the tests react to data changes of varying magnitude. I choose a use case from a Fraud detection monitoring layer to demonstrate how a test&#x27;s sensitivity can dramatically affect the outcome of the monitoring layer. Sivan Milwerger, Head Of Data Analysis &amp; BI at StreamElementsAn A/B test is a research method used to compare the performance of two or more product, feature, or marketing campaign variations.While many organizations want to make data-driven decisions, A/B test tools are not implemented in every organization, and sometimes the first analyst will be the one who will have to design and build the experiment. In the lecture, I will demonstrate how to design and analyze an A/B test when no automatic tools exist.Yoav Nordman, Tech Lead &amp; Architect at TikalAnomaly Detection with Apache Pinot and Thirdeye.Thirdeye monitors your data in near real time, detects anomalies and gives insights and interactive root-cause analysis into why they are happening. I will explain the technology and what makes it unique and show a quick demo at the end of the lecture using live data.Erez Filosof Timnat, Data Scientist Team Leader at AppsFlyer Embarking on a data science project can be exciting and challenging, but it’s important to understand the different stages involved in its lifecycle. From exploring and cleaning data to building models, evaluating them, and finally, productizing the solution, there are several critical steps to consider.In this talk, I&#x27;ll dive into the common Crisp-DM method for data science project management and explore how it can be adjusted to modern practices. I&#x27;ll also discuss some key points to consider to ensure your project&#x27;s success, such as: How to manage expectations, fail fast, iterate quickly, collaborate effectively with stakeholders and more. Join me to explore the life of a data science project and discover the best practices for successfully managing it from start to finish.Shaked Holzman, Technical Account Manager at AWSGenerative AI is a type of AI that can create new content and ideas, including conversations, stories, images, videos, and music.Like all AI, generative AI is powered by ML models- very large models that are pretrained on large amount of data and commonly referred to as Foundation Models (FMs).In this talk, I&#x27;ll introduce Amazon Bedrock- AWS managed service that can be used to build and scale generative AI applications with foundations models

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