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In today's business world, data is everything. From customer information to financial records, businesses rely on data to make critical decisions and stay ahead of the competition. However, this data is also a valuable target for hackers and criminals. In this 45-minute webinar you’ll learn how Neo4j enables you to safeguard your most important asset, your data.
Join us for this webinar designed specifically for data scientists and Cloud Architects to learn about Neo4j’s leading graph technology and Google Cloud Vertex AI to apply graph feature engineering to your own datasets. We’ll walk through just a few simple steps to deploy Neo4j and Vertex AI on Google Cloud to begin building a machine learning pipeline with a real-world dataset powered by Neo4j Graph Data Science to improve predictions in Google Cloud Vertex AI.
Neo4j Graph Data Science is a powerful platform, purpose-built to extract meaning from data relationships to answer business-critical questions. Join us for a 45-minute webinar to hear about key features of the newest release, now available fully managed on Google Cloud Platform.
Implemented in production-grade within the Neo4j Graph Data Science library, Graph Embeddings are an advanced AI technology used to translate your connected data – knowledge graphs, customer journeys, and transaction networks – into a predictive signal. Applications of Graph Embeddings are numerous: finding fraud, entity resolution and disambiguation, improving product recommendations, discovering new drugs and predicting churn.
The largest investigation in journalistic history, the Pandora Papers revealed a shadow financial system implicating hundreds of well-known figures. Join us for 75-minutes for an interview with ICIJ journalists Emilia Diaz-Struck and Miguel Fiandor Gutiérrez, who share how graph-based analytics made it all possible.
Les données produites dans le secteur pharmaceutique sont très hétérogènes, obtenues expérimentalement, générées à partir de modèles d'intelligence artificielle et sont de provenances multiples. Nous concevons et développons le graphe de connaissances Pegasus qui vise, en plus de capitaliser sur des données actuellement disponibles, à explorer l'environnement complexe des cibles thérapeutiques, à identifier des modalités de criblage pertinentes pour les moduler et à soutenir le dérisquage de nouvelles modalités expérimentales. Dans un premier temps nous détaillons la conception du modèle de données de Pegasus. Puis, nous exposons des applications industrielles de Pegasus pour des projets thérapeutiques exploratoires. La première application porte sur la caractérisation des effets hors cibles de perturbateurs. La deuxième porte sur le dérisquage d’une nouvelle modalité expérimentale. La troisième détaille la conception de librairies focalisées pour le criblage de perturbateurs. Enfin, la quatrième application précise l’utilisation envisagée de signatures phénotypiques obtenues à partir de campagnes de criblages à haut contenu afin de relier les mondes biologiques, chimiques et phénotypiques. Dans l'ensemble, le graphe de connaissances Pegasus et ses applications soutiennent la prise de décisions et supportent les phases primaires de recherche et de découverte de nouveaux médicaments.
Women are leading innovation with knowledge graphs across industries, shaping the data landscape for years to come. This must-attend event features women leaders from top enterprises who will discuss their experience developing solutions with knowledge graphs.
La blockchain a de nombreuses applications aujourd’hui, et pas uniquement dans les cryptomonnaies. Ce registre est partagé simultanément par tous ses utilisateurs et permet de sécuriser les transactions grâce au chiffrement et à la décentralisation. Les cas d’utilisation de la blockchain sont nombreux et multi-sectoriels : Banque : validation des transactions Assurance : automatisation des procédures de remboursement Logistique et agro-alimentaire : traçabilité des produits Energie : échange de services et de valeurs dans des réseaux locaux de production et de revente d’énergie Art et jeux vidéos : digitalisation d’oeuvres échangeables via des tokens Le système de blockchain est en lui-même un graphe qui contient l’historique de toutes les transactions, les nouveaux blocs de données étant “chaînés”, reliés aux blocs existants. Dans le cas des cryptomonnaies, la blockchain sert à vérifier et valider les transactions. Enfin, les NFT, “Non-Fungible Tokens” sont des certificats numériques infalsifiables qui attestent l’authenticité d’un objet virtuel (metavers, tweet de personnalité, etc.). Les NFT font l’objet de grandes spéculations au sein de la blockchain Ethereum. C’est ce monde fascinant des NFT et de la blockchain que l’on vous propose de vulgariser et d’explorer dans un graphe de connaissances Neo4j. Romain Dumaine, SDR chez Neo4j et spécialiste de la blockchain, sera accompagné de Nicolas Rouyer, Pre-sales chez Neo4j, pour décrypter les cryptomonnaies, ouvrir le capot de la blockchain et comprendre l’essor phénoménal des NFT.
With the world’s supply chain system in crisis, it’s clear that better solutions are needed. Digital twins built on knowledge graph technology allow you to achieve an end-to-end view of the process, supporting real-time monitoring of critical assets. Join Dr. Maya Natarajan and Dr. Michael Moore for this 60-minute webinar, where you’ll learn what a knowledge graph is and how to use it as a digital twin for supply chain management.
La Graph Data Science (GDS) sfrutta la potenza delle connessioni tra i dati stessi per analizzare le loro relazioni, analizzare i pattern presenti nei dati ed effettuare facilmente analisi d'impatto sulla rete. Unisciti a noi per questo webinar in cui discuteremo: Come le reti siano facilmente modellabile sotto forma di grafo; Come la GDS possa essere uno strumento fondamentale per l'analisi delle relazioni esistenti nel grafo e di come i risultati ne riflettano i benefici; Come permettere di visualizzare graficamente la rete stessa (anche a personale non tecnico) con il minimo sforzo e con il supporto di un suggeritore automatico dei pattern presenti nei dati.
Many organizations in the manufacturing space are using Neo4j to solve core AI use cases. Do you want to learn how to use graph technology for a competitive advantage? Join us for a 60-minute session on how Neo4j Aura, the leading graph database as a service on Google Cloud, is enabling manufacturing organizations to address key data management challenges – from supply chain optimization, to warranty, fraud, and process automation.
Do you want to use graphs in your machine learning pipelines or analytics dashboards but wonder about feasibility for larger volumes of data? Neo4j built Graph Data Science from the ground up to handle the biggest datasets you can come up with. Join this event to learn all the tips and tricks to running graph algorithms quickly and effectively at scale.
Pour vos expérimentations comme pour vos projets les plus critiques, retrouvez toute la puissance, la flexibilité et la sécurité de Neo4j dans une solution entièrement gérée. AuraDB, c’est un environnement Neo4j sans contrainte d’administration, disponible à l’échelle globale. Durant ce webinaire, nous découvrirons par la pratique toutes les étapes allant de l’inscription au service AuraDB jusqu’au déploiement de votre application Avec Pierre Haltermeyer
Neo4j Bloom streamlines conversations and projects across teams. Its illustrative, codeless search-to-storyboard design makes Neo4j Bloom the ideal interface for non-technical project participants to share in the innovative work of their graph analytics and development teams. In this 45-minute session, you’ll learn about Neo4j Bloom from Jeff Gagnon, Product Manager at Neo4j. Jeff will take you through the mechanics of Neo4j Bloom and show how easy it is for businesses to explore and investigate their graph data.
Neo4j 4.4, the latest release from Neo4j, empowers developers by giving them full control over how their applications interact with the database – including robust data pipelines, machine learning (ML) and AI, streaming data from sources like Kafka, and more. Join us for a special webinar on Neo4j 4.4 and how it builds on the most trusted, scalable, secure database for performance and data integrity. In this 45-minute session, Ivan Zoratti (Director of Product Management at Neo4j) and Stu Moore (Product Manager at Neo4j) will walk you through the new additions and feature enhancements, provide a demo of 4.4, and answer all your questions.
Watch Maya Natarajan, Senior Director Knowledge Graphs, at Neo4j; George Eapen, Group CISO at Petrofac and Edosa Odaro, Head of Data at AIG, in discussion as they share their insights and experiences on Visualising an Intelligent, Secure Supply Chain. Moderator: Jon Bernstein, Technology Journalist, Editor and Moderator Roundtable organised by HotTopic's
Dans ce webinaire vous découvrirez : - comment passer à l'échelle quand on a des données connectées très volumineuses - comment déployer un graphe distribué dans le cloud - comment régler les problèmes réseau rencontrés Prêts à passer à l'échelle en production avec Neo4j ?
Marion Robin & Simone Bom - Deloitte NL In this webinar, we will showcase the best strategies to initiate graph projects and share some of the analytics solutions we have developed with our customers. We will deep dive into two use cases where connections make a difference: - Data quality management for highly complex manufacturing products: Here we will show how Graph technology can improve data quality by accelerating data inconsistency detection and reporting in densely connected data, using the example of Bill of Materials. - Smart assistant for online customers: With this use case we will show the impact of connected data for expert recommendations and how it can be combined with chatbots for improved online customer experience
Maybe you’ve heard about using graph data, or relationships, in machine learning pipelines for better accuracy or new types of predictions. But you’ve probably wondered: how exactly do I improve my predictive accuracy? The answer is graph embeddings: a technique to translate your graph into the right representation of your problem. In this webinar, Dr. Alicia Frame will present on graph embeddings in Neo4j: a data encoding technique that allows you to make highly accurate predictions based on graph structure. Zach Blumenfeld will do a demo of graph embeddings from a consumer data set, showing you how to make accurate recommendations.
Dans ce webinaire, nous allons connecter un chatbot avec Neo4j, soit une interface en langage naturel pour parler à ses données connectées ! Nous aborderons l'écosystème technique, la plateforme prisme.ai, une API GraphQL, et bien sûr Neo4j.
L'émergence du web 2.0 et des réseaux sociaux a vu l’utilisateur devenir non seulement un consommateur, mais aussi un créateur de contenu. C’est donc naturellement par la modélisation en graphe, et en considérant l’utilisateur comme l’un des sommets du graphe induit par les relations qu’il entretient avec ses semblables, que l’on peut tirer le meilleur parti de ces données. Les méthodes d’analyse des réseaux sociaux, situées au confluent de la sociologie, du data mining et de la théorie des graphes, sont les principaux outils à notre disposition. Neo4j, base de données graphe native, munie de son écosystème analytique et de sa bibliothèque Graph Data Science (GDS), sont le choix incontournable de tout professionnel désirant réaliser une telle tâche de manière productive et scalable. Nous visiterons, au sein de ce webinaire, les différents cas d’usage, approches et méthodes de l’analyse des réseaux sociaux. Puis, via une démonstration basée sur un corpus de tweets, nous montrerons la pertinence de Neo4j/GDS pour détecter l’émergence d’une tendance ou d’un fait à partir de signaux faibles issus des médias sociaux.
Using data to make the most accurate, advantageous decisions has long been a critical part of any organisation’s business strategy. However, when the amount of data has grown to be almost unmanageable, is kept in multiple silos, and is subject to complex regulation, gaining competitive advantage becomes incredibly difficult. Combining graph databases and artificial intelligence seems like a way forward. In this webinar, we will take a look at what a graph database is, why it is a key part of a highly connected data world and how you can use technologies & services from Google Cloud and partners to make sense of this huge amount of data.
Ensuring compliance with laws, regulations, and policies is crucial for many heavily regulated institutions including banks, insurance companies, and government agencies. By efficiently combining different data sources and increasing data quality, knowledge graphs surface critical, actionable insights from complex connections and patterns that naturally occur in regulatory compliance data. The extracted insights have the potential of significantly improving, simplifying, and speeding up compliance efforts in any regulated organisation. In this presentation, we briefly introduce knowledge graphs and explain why they are relevant to many compliance issues. We then show a real-world example of a governance knowledge graph for a regulated government institution, explaining how it has been built and what value it has provided to the organization. We will discuss the challenges encountered along the way and how they have been tackled using Neo4j and GraphAware Hume.
This 60-minute webinar, Link Prediction: Fill the Blanks and Predict the Future!, is a deep dive on link prediction and new features in version GDS 1.7, which adds the ability to define and run pipelines for supervised machine learning. Alicia Frame and Zach Blumenfeld will cover how link prediction works, walk through considerations for sparse or biased data, and demo a real-world data set. This webinar is intended for data scientists – both new to using graphs in data science as well as experts looking to wring a few extra percentage points of accuracy out of data.
In this 60-minute conversation with IDC, we will highlight the momentum and reasons why a graph data platform is a breakthrough solution for businesses in need of a flexible data model. Please join Mohit Sagar, Group Managing Director of CIO Network, as he hosts the conversation with Dr. Christopher Lee Marshall, Associate VP at IDC, and Nik Vora, Vice President of APAC at Neo4. During this very exciting discussion, you'll discover the insights and knowledge unlocked with the graph data platform.
Loading data into Neo4j often starts with a number of simple scripts. As your project grows, so does the number and size of your scripts: they quickly become hard to scale, hard to manage, and hard to maintain. Apache Hop is a new open-source data orchestration platform that was designed from the ground up to tackle these problems. In Hop, data developers visually design data pipelines that can run on the Hop native engine, or on Spark, Flink, Google Dataflow over Apache Beam. Hop has built-in life cycle management functionality to help you manage your projects and environments, version control with git, run unit tests, and more. Apache Hop is unparalleled in its support for Neo4j with over 20 plugins to design, write, and read from your graphs. What you’ll learn in this webinar: What is Apache Hop and how it works How to load data into and extract data from Neo4j with Apache Hop How Apache Hop pipelines scale with any workload and volume of data How Apache Hop handle data lineage, unit testing, version control, deployment life cycle, and much more
This 45-minute webinar will show how the partnership between Neo4j and Google Cloud provides best-in-class technology to address data challenges in the financial services industry. Hosted by Dr. Jim Webber, Chief Scientist and CTO, Field Ops at Neo4j, this session features Antoine Larmanjat, Technical Director, Office of the CTO, at Google Cloud on the importance of cloud-based, graph-powered AI and key considerations for data strategy.
This 45-minute webinar on knowledge graphs features an interview with the authors of Neo4j’s new book, Knowledge Graphs: Data in Context for Responsive Businesses. Moderated by subject matter expert Dr. Maya Natarajan, book authors Dr. Jesús Barrasa, Amy Hodler, and Dr. Jim Webber will discuss how to drive key business decisions using the contextual knowledge derived from a knowledge graph.
Heard about graph databases? Curious about what they are and how they work? Want to know where they’re best used? Then this is the session for you! In this workshop we will: - Introduce you to graph databases - Cover approaches for identifying graph-shaped problems - Get our hands on our very first graph database experience where we will load and query data, using Neo4j Aura Free Useful links: - Neo4j Aura Free: https://dev.neo4j.com/aura-login - Slides: https://www.slideshare.net/neo4j/training-week-introduction-to-neo4j - Article on relational versus graph databases: https://dev.neo4j.com/rdbms-gdb
Neo4j, the leading enterprise graph platform, is now globally available on Amazon Web Services (AWS) as a fully managed, always-on database service. Neo4j Aura Enterprise on AWS empowers organizations to rapidly build mission-critical, intelligent cloud-based applications backed by the performance, scale, security, and reliability that only the most deployed and most trusted graph technology can provide. Customers like Levi Strauss & Co., Sainsbury’s, Siemens, The Orchard and Tourism Media are already using Aura Enterprise on AWS for fraud detection, regulatory compliance, recommendation engines, supply chain analysis, and much more. Watch this exclusive digital event to learn more about Neo4j Aura Enterprise on AWS. 00:00 Welcome & Introductions 02:16 The State of the Data and Analytics Market 15:13 Customer Panel Discussion: Unlocking the Power of Graph Databases on AWS 44:00 Building Modern Graph Applications with Neo4j Aura in the Cloud
Today’s modern, intelligent applications require tools that enable rapid application development and tight integration between the front and backend. At this 45-minute session led by Gregory King, Product Manager at Neo4j, you’ll learn about two key tools: Neo4j Desktop and Neo4j Browser. Join us for an overview of these tools and how to use them, the latest features, and new release information.
This 60-minute conversation with Forrester will unpack their decision to highlight graph data platforms in The Forrester Wave. We’ll also discuss why companies and governments worldwide are rapidly adopting graph data platforms and how they’re being used to drive growth and uncover hidden value in data.
Neo4j 4.3 is the latest update to Neo4j, the leading graph database for deployment in any production environment – on-premises, hybrid, or in the cloud. Join us for a special webinar exploring what’s new with Neo4j 4.3 and explaining how it builds on the most trusted, scalable, secure database for performance and data integrity. In this 45-minute session, Ivan Zoratti, Director of Product Management at Neo4j, will show you a demo of 4.3 and answer your questions.
This exciting 60-minute webinar features graph experts discussing the past, present, and future of artificial intelligence (AI). Neo4j Chief Scientist Dr. Jim Webber will be hosting Dr. Jianshu Weng, Head of SecureAI at AI Singapore, and David Berend, Google scholar and doctoral candidate at the Nanyang Technological University of Singapore, in an eye-opening discussion of AI and the Graph Data Platform.
Avec l’adoption croissante de la technologie des bases de données de graphes dans tous les secteurs d’activité, il est fondamental de comprendre les avantages de cette technologie afin de vous lancer avec les graphes. Dans ce webinaire d’introduction, nous présenterons la plateforme de graphes Neo4j, une suite d’applications et d’outils qui permettent de tirer parti des données, en mettant l’accent sur les outils suivants : La base de données de graphes Neo4j, une base de données de graphes native qui permet d’exploiter les données ainsi que leurs relations Neo4j Bloom, une application de visualisation facile à utiliser pour explorer et analyser les données de graphes dans différentes perspectives Dans ce webinaire de 45 minutes, vous découvrirez : La technologie Neo4j - le langage Cypher Les cas d’usage Neo4j - ou comment les relations créent de la valeur La visualisation de graphes Neo4j avec Bloom Une démonstration autour d’un graphe de connaissances
This 45-minute webinar will look into how the COVID-19 pandemic has caused fundamental changes in consumer behavior, supply chains, and routes to markets. You’ll learn that with a combination of knowledge graphs and graph-based analytics, supply chain companies can bring complex products to market on schedule, proactively take action to remediate potential issues, and mitigate risks through greater end-to-end visibility.
Graphs are everywhere. From marketing to operations to healthcare, the graph market is expected to boom to ~$2,5 billion by 2024. It is not a question of if graphs have a use case within your company but how to successfully deploy, govern and maintain your graph solution or application. Often beginning with a flagship use case that solves a business challenge, we will share our experience on how to demonstrate the value of graph technology within your company, define a clear strategy towards incorporating graphs into your data landscape, and being confident of the results your graph produces. Join Elaine Fannoÿ and Magali Thésias from Deloitte Belgium to dive into how best practices of design thinking and agile methodologies find their way into graph technologies.
Neo4j Bloom streamlines conversations and projects across teams. Its illustrative, codeless search-to-storyboard design makes Neo4j Bloom the ideal interface for non-technical project participants to share in the innovative work of their graph analytics and development teams. In this 45-minute session, you will learn about Neo4j Bloom from Anurag Tandon, Director of Product Management at Neo4j. Anurag will take you through the mechanics of the Neo4j Bloom, showing how easy it is for businesses to explore their applications through visual interaction with graphs.
Relationships are highly predictive of behavior, yet most data science models overlook this information because it's difficult to extract network structure for use in machine learning (ML). With graphs, relationships are embedded in the data itself, making it practical to add these predictive capabilities to your existing practices. That’s why we’re presenting and demoing the use of graph-native ML to make breakthrough predictions. This will cover: Different approaches to graph feature engineering, from queries and algorithms to embeddings How ML techniques leverage everything from classical network science to deep learning and graph convolutional neural networks How to generate representations of your graph using graph embeddings, create ML models for link prediction or node classification, and apply these models to add missing information to an existing graph/incoming data Why no-code visualization and prototyping is important
Intelligent applications require APIs and libraries that are open, flexible, and easy to build, while also providing tighter integration between the front-end and backend. In this 45-minute session you’ll learn about Neo4j GraphQL Library from Anurag Tandon, Director of Product Management, Neo4j, and Darrell Warde, Software Engineer, Neo4j GraphQL Library. They’ll take you through the mechanics of the library, showing you how this extensible, low-code, open source JavaScript library enables rapid API development for cross-platform and mobile applications by tapping into the power of connected data.
This 45-minute webinar will highlight how Neo4j Aura on Google Cloud is the perfect solution for cloud developers who want to gain insight from their data, regardless of location. We’ll hear from Kelly Stirman, Director, Outbound PM for Databases at Google Cloud, who will explain the importance of integration and connectivity with Google Cloud architecture, covering key considerations for data strategy. He’ll be joined by David Allen, Partner Solution Architect at Neo4j, who will discuss the top use cases and Neo4j’s cloud strategy as it pertains to the product roadmap.
Hosted by Neo4j Chief Scientist, Dr. Jim Webber, this event features two VPs of the APAC region – Richard Jones from Dataiku and Nik Vora from Neo4j – as they share insights from their experience using graphs with digital twins.
Join us to hear about new supervised machine learning (ML) capabilities in Neo4j and learn how to train and store ML models in Neo4j with the Graph Data Science library (GDS). Most data science models ignore network structure, but graph technology helps create highly predictive features to ML models, which increase accuracy and answer complex questions based on relationships. The latest GDS update (v1.5) provides a new end-to-end model-building pipeline entirely in Neo4j so you can take advantage of state-of-the-art ML techniques and continually update your graph – all without leaving Neo4j. In this session, we’ll walk through how to generate representations of your graph using graph embeddings, create ML models for link prediction or node classification, and apply these models to add missing information to an existing graph or incoming graph data. You’ll also hear about other recent updates including new graph algorithms and memory optimization. Your questions will be answered throughout the webinar! Presenters: Dr. Alicia Frame and Amy Hodler
