Michael Moore Ph.D., Principal, Partner Solutions and Neo4j Technology, Neo4j
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.
Optimizing the Supply Chain with Knowledge Graphs, IoT and Digital Twins_Moor...Neo4j
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.
Optimizing Your Supply Chain with the Neo4j GraphNeo4j
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.
This document discusses optimizing supply chains with Neo4j graph databases. It notes that supply chains have complex relationships that are naturally modeled as graphs. It promotes Neo4j's graph database, tools, and algorithms for building digital twins of supply chains for purposes like visibility, optimization, and calculating scope 3 carbon emissions. Examples are given of companies using Neo4j for supply chain applications like routing, asset management, and equipment maintenance.
The Neo4j Data Platform for Today & Tomorrow.pdfNeo4j
The document discusses the Neo4j graph data platform. It highlights that connected data is growing exponentially and graphs are well-suited to model real-world relationships. Neo4j provides a native graph database, tools, and services to store, query, and analyze graph data. Key capabilities include high performance, flexible schemas, developer productivity, and supporting transactions and analytics workloads.
Using Connected Data and Graph Technology to Enhance Machine Learning and Art...Neo4j
This document discusses how using connected graph data and graph technologies can improve machine learning and artificial intelligence. It notes that relationships are highly predictive and underutilized in data, and that graphs provide a natural way to store and leverage relationship data. Graph databases allow incorporating these relationships into predictive models to produce more accurate results.
Optimizing Your Supply Chain with Neo4j
Dr. Michael Moore, Senior Director, Strategy and Innovation, Neo4j
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.
El camino hacia el éxito con las bases de datos de grafos, la ciencia de dato...Neo4j
This document discusses using graph databases, graph data science, and generative AI to unlock insights from connected data. It highlights how relationships in data are valuable, and how graph databases provide an intuitive way to represent and query relationship data. The document introduces Neo4j's graph database capabilities, including graph algorithms for analytics, machine learning on graphs, and integration with other data systems. It also discusses using Neo4j to ground language models for more accurate generative AI applications.
Amsterdam - The Neo4j Graph Data Platform Today & TomorrowNeo4j
This document provides an overview of the Neo4j Graph Data Platform. Some key points:
- Neo4j is a native graph database that is well-suited for connected data use cases that are growing exponentially. Graph databases can handle relationships better than relational databases and support relationship queries better than NoSQL databases.
- The Neo4j Graph Data Platform includes the native graph database, development tools, data science and analytics capabilities, and an ecosystem of integrations. It can be deployed anywhere including as a service on AuraDB.
- Neo4j has pioneered the graph database category since 2010 and continues to drive innovation with features like graph-RBAC security, graph data
Optimizing the Supply Chain with Knowledge Graphs, IoT and Digital Twins_Moor...Neo4j
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.
Optimizing Your Supply Chain with the Neo4j GraphNeo4j
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.
This document discusses optimizing supply chains with Neo4j graph databases. It notes that supply chains have complex relationships that are naturally modeled as graphs. It promotes Neo4j's graph database, tools, and algorithms for building digital twins of supply chains for purposes like visibility, optimization, and calculating scope 3 carbon emissions. Examples are given of companies using Neo4j for supply chain applications like routing, asset management, and equipment maintenance.
The Neo4j Data Platform for Today & Tomorrow.pdfNeo4j
The document discusses the Neo4j graph data platform. It highlights that connected data is growing exponentially and graphs are well-suited to model real-world relationships. Neo4j provides a native graph database, tools, and services to store, query, and analyze graph data. Key capabilities include high performance, flexible schemas, developer productivity, and supporting transactions and analytics workloads.
Using Connected Data and Graph Technology to Enhance Machine Learning and Art...Neo4j
This document discusses how using connected graph data and graph technologies can improve machine learning and artificial intelligence. It notes that relationships are highly predictive and underutilized in data, and that graphs provide a natural way to store and leverage relationship data. Graph databases allow incorporating these relationships into predictive models to produce more accurate results.
Optimizing Your Supply Chain with Neo4j
Dr. Michael Moore, Senior Director, Strategy and Innovation, Neo4j
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.
El camino hacia el éxito con las bases de datos de grafos, la ciencia de dato...Neo4j
This document discusses using graph databases, graph data science, and generative AI to unlock insights from connected data. It highlights how relationships in data are valuable, and how graph databases provide an intuitive way to represent and query relationship data. The document introduces Neo4j's graph database capabilities, including graph algorithms for analytics, machine learning on graphs, and integration with other data systems. It also discusses using Neo4j to ground language models for more accurate generative AI applications.
Amsterdam - The Neo4j Graph Data Platform Today & TomorrowNeo4j
This document provides an overview of the Neo4j Graph Data Platform. Some key points:
- Neo4j is a native graph database that is well-suited for connected data use cases that are growing exponentially. Graph databases can handle relationships better than relational databases and support relationship queries better than NoSQL databases.
- The Neo4j Graph Data Platform includes the native graph database, development tools, data science and analytics capabilities, and an ecosystem of integrations. It can be deployed anywhere including as a service on AuraDB.
- Neo4j has pioneered the graph database category since 2010 and continues to drive innovation with features like graph-RBAC security, graph data
Nordics Edition - The Neo4j Graph Data Platform Today & TomorrowNeo4j
Neo4j provides a graph data platform for modeling and querying connected data. The platform includes a native graph database, graph query language, and tools for data science, analytics, and application development. Recent innovations include machine learning pipelines, improved Python client, and new algorithms to unlock insights from relationships in the data.
Get Started with the Most Advanced Edition Yet of Neo4j Graph Data ScienceNeo4j
The document discusses Neo4j's graph data science capabilities. It highlights that Neo4j provides tools for graph algorithms, machine learning pipelines for tasks like node classification and link prediction, and a graph catalog for managing graph projections from the underlying database. The document also notes that Neo4j's capabilities allow users to leverage relationships in connected data to answer business questions.
The document outlines an agenda for a workshop on building a graph solution using a digital twin data set. It includes sections on logistics, introductions, explaining the use case of a digital twin for a rail network, modeling the graph database solution, building the solution, and a question and answer period. Key aspects covered include an overview of Neo4j's graph database capabilities, modeling the domain entities and relationships, and exploring sample data related to operational points, sections, and points of interest for a rail network digital twin use case.
GPT and Graph Data Science to power your Knowledge GraphNeo4j
In this workshop at Data Innovation Summit 2023, we demonstrated how you could learn from the network structure of a Knowledge Graph and use OpenAI’s GPT engine to populate and enhance your Knowledge Graph.
Key takeaways:
1. How Knowledge Graphs grow organically
2. How to deploy Graph Algorithms to learn from the topology of a graph
3. Integrate a Knowledge Graph with OpenAI’s GPT
4. Use Graph Node embeddings to feed Machine Learning workflow
The Data Platform for Today's Intelligent Applications.pdfNeo4j
Do you know how graph technology is used in today’s data-driven applications? We’ll get you up to speed and introduce you to the Neo4j product portfolio.
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The document discusses Neo4j's new Graph Data Science as a Service (GDSaaS) product called AuraDS. AuraDS provides full access to Neo4j's Graph Data Science platform and algorithms in a fully managed cloud service, allowing users to focus on analytics instead of database administration. It introduces the key capabilities and integration options available through AuraDS.
Webinar - IA generativa e grafi Neo4j: RAG time!Neo4j
Here are the key limitations of using vector databases for RAG:
1. Schema-less - Vector databases don't enforce a schema, making it difficult to represent structured knowledge like entities, relationships and properties.
2. Indexing challenges - It's hard to efficiently index and retrieve data based on semantic relationships rather than just keywords.
3. Explainability - Without an explicit graph structure, it's difficult to explain how a particular piece of retrieved data is relevant or related to the user's question.
4. Knowledge representation - Vector spaces are not well-suited for representing hierarchical, multi-relational knowledge like you would find in a knowledge graph.
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The document discusses using generative AI and knowledge graphs. It explains how large language models (LLMs) can be grounded in knowledge graphs to improve accuracy by providing context. Neo4j is proposed as a knowledge graph that can be used to ground LLMs by supplying domain-specific information to generate more accurate responses. Integrating LLMs with Neo4j's graph capabilities could improve accuracy, allow models to be deployed with confidence due to security and scalability, and unlock innovation through interoperability.
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Dr Jesús Barrasa
Head of Field Engineering, Neo4j
Dr Jim Webber
Chief Scientist, Neo4j
Gartner IT Symposium Xpo Barcelona 2022 - Neo4j
In diesem Webinar wollen wir einen Überblick über unser Angebot für Data Scientsts geben und zeigen, was heute schon relativ einfach und schnell möglich ist.
La bi, l'informatique décisionnelle et les graphesCédric Fauvet
The document discusses how graph databases and graph technologies can be used for business intelligence, analytics, and decision making. It provides examples of how companies in various industries like communications, logistics, online recruiting, and consumer web have used graph databases from Neo4j to power applications, gain insights, and improve user experiences. Specific use cases discussed include network management, parcel routing, social job search, recommendations, and interactive television programming. The benefits of the graph model over relational databases for complex connected data are also highlighted.
The document discusses how graph databases are well-suited for telecommunications networks due to their ability to model complex relationships. It provides examples of large telecom companies using Neo4j to power applications involving network topology analysis, digital twins, and other use cases. Key benefits highlighted include stronger support for dynamic connectivity modeling, improved path calculation, and more flexible approaches compared to relational databases. The presentation outlines Neo4j's adoption among top telecom firms and leadership in the graph database market.
This document outlines an upcoming workshop on graph technology and data science using Neo4j. The workshop will cover knowledge graphs, graph algorithms, graph machine learning techniques, and use cases. It will include demonstrations of algorithms like node similarity and centrality measures on graphs. Attendees will learn how graph databases like Neo4j can power graph analytics and machine learning to gain insights from connected data.
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This introduction to graph databases is specifically designed for Enterprise Architects who need to map business requirements to architectural components like graph databases. It explains how and why graphs matter for Enterprise Architecture and reviews the architectural differences between relational and graph models.
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Graph databases are well-suited for tracking carbon emissions across complex supply chains. Building a graph digital twin allows organizations to:
1. Model the entities and relationships in their value chain to calculate scope 3 emissions.
2. Ingest multiple data sources and map emission factors to estimate upstream and downstream carbon impacts.
3. Continuously improve data quality and refine carbon accounting as more information is added to the unified graph model.
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Neo4j provides a graph data platform for modeling and querying connected data. The platform includes a native graph database, graph query language, and tools for data science, analytics, and application development. Recent innovations include machine learning pipelines, improved Python client, and new algorithms to unlock insights from relationships in the data.
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The document discusses Neo4j's graph data science capabilities. It highlights that Neo4j provides tools for graph algorithms, machine learning pipelines for tasks like node classification and link prediction, and a graph catalog for managing graph projections from the underlying database. The document also notes that Neo4j's capabilities allow users to leverage relationships in connected data to answer business questions.
The document outlines an agenda for a workshop on building a graph solution using a digital twin data set. It includes sections on logistics, introductions, explaining the use case of a digital twin for a rail network, modeling the graph database solution, building the solution, and a question and answer period. Key aspects covered include an overview of Neo4j's graph database capabilities, modeling the domain entities and relationships, and exploring sample data related to operational points, sections, and points of interest for a rail network digital twin use case.
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In this workshop at Data Innovation Summit 2023, we demonstrated how you could learn from the network structure of a Knowledge Graph and use OpenAI’s GPT engine to populate and enhance your Knowledge Graph.
Key takeaways:
1. How Knowledge Graphs grow organically
2. How to deploy Graph Algorithms to learn from the topology of a graph
3. Integrate a Knowledge Graph with OpenAI’s GPT
4. Use Graph Node embeddings to feed Machine Learning workflow
The Data Platform for Today's Intelligent Applications.pdfNeo4j
Do you know how graph technology is used in today’s data-driven applications? We’ll get you up to speed and introduce you to the Neo4j product portfolio.
New! Neo4j AuraDS: The Fastest Way to Get Started with Data Science in the CloudNeo4j
The document discusses Neo4j's new Graph Data Science as a Service (GDSaaS) product called AuraDS. AuraDS provides full access to Neo4j's Graph Data Science platform and algorithms in a fully managed cloud service, allowing users to focus on analytics instead of database administration. It introduces the key capabilities and integration options available through AuraDS.
Webinar - IA generativa e grafi Neo4j: RAG time!Neo4j
Here are the key limitations of using vector databases for RAG:
1. Schema-less - Vector databases don't enforce a schema, making it difficult to represent structured knowledge like entities, relationships and properties.
2. Indexing challenges - It's hard to efficiently index and retrieve data based on semantic relationships rather than just keywords.
3. Explainability - Without an explicit graph structure, it's difficult to explain how a particular piece of retrieved data is relevant or related to the user's question.
4. Knowledge representation - Vector spaces are not well-suited for representing hierarchical, multi-relational knowledge like you would find in a knowledge graph.
A graph database overcomes these limitations by providing an
La strada verso il successo con i database a grafo, la Graph Data Science e l...Neo4j
The document discusses using generative AI and knowledge graphs. It explains how large language models (LLMs) can be grounded in knowledge graphs to improve accuracy by providing context. Neo4j is proposed as a knowledge graph that can be used to ground LLMs by supplying domain-specific information to generate more accurate responses. Integrating LLMs with Neo4j's graph capabilities could improve accuracy, allow models to be deployed with confidence due to security and scalability, and unlock innovation through interoperability.
Are You Underestimating the Value Within Your Data? A conversation about grap...Neo4j
Are You Underestimating the Value Within Your Data?
A conversation about graph technology
Dr Jesús Barrasa
Head of Field Engineering, Neo4j
Dr Jim Webber
Chief Scientist, Neo4j
Gartner IT Symposium Xpo Barcelona 2022 - Neo4j
In diesem Webinar wollen wir einen Überblick über unser Angebot für Data Scientsts geben und zeigen, was heute schon relativ einfach und schnell möglich ist.
La bi, l'informatique décisionnelle et les graphesCédric Fauvet
The document discusses how graph databases and graph technologies can be used for business intelligence, analytics, and decision making. It provides examples of how companies in various industries like communications, logistics, online recruiting, and consumer web have used graph databases from Neo4j to power applications, gain insights, and improve user experiences. Specific use cases discussed include network management, parcel routing, social job search, recommendations, and interactive television programming. The benefits of the graph model over relational databases for complex connected data are also highlighted.
The document discusses how graph databases are well-suited for telecommunications networks due to their ability to model complex relationships. It provides examples of large telecom companies using Neo4j to power applications involving network topology analysis, digital twins, and other use cases. Key benefits highlighted include stronger support for dynamic connectivity modeling, improved path calculation, and more flexible approaches compared to relational databases. The presentation outlines Neo4j's adoption among top telecom firms and leadership in the graph database market.
This document outlines an upcoming workshop on graph technology and data science using Neo4j. The workshop will cover knowledge graphs, graph algorithms, graph machine learning techniques, and use cases. It will include demonstrations of algorithms like node similarity and centrality measures on graphs. Attendees will learn how graph databases like Neo4j can power graph analytics and machine learning to gain insights from connected data.
GraphSummit Toronto: Keynote - Innovating with Graphs Neo4j
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Learn about the importance of graph technology, its evolution over the last few years and the impact it has had on the database and data analytics industry. This session will provide an overview of graph technology and talk about the past, present, and future of graphs and data management. Multiple use cases and customer examples will be covered, including examples of where graph databases and graph data science can assist and accelerate machine learning and artificial intelligence projects.
This introduction to graph databases is specifically designed for Enterprise Architects who need to map business requirements to architectural components like graph databases. It explains how and why graphs matter for Enterprise Architecture and reviews the architectural differences between relational and graph models.
Actionable Carbon Tracking and Analysis with the Neo4j Graph Data PlatformNeo4j
Graph databases are well-suited for tracking carbon emissions across complex supply chains. Building a graph digital twin allows organizations to:
1. Model the entities and relationships in their value chain to calculate scope 3 emissions.
2. Ingest multiple data sources and map emission factors to estimate upstream and downstream carbon impacts.
3. Continuously improve data quality and refine carbon accounting as more information is added to the unified graph model.
How Graph Data Science can turbocharge your Knowledge GraphNeo4j
Knowledge Graphs are becoming mission-critical across many industries. More recently, we are witnessing the application of Graph Data Science to Knowledge Graphs, offering powerful outcomes. But how do we define Knowledge Graphs in industry and how can they be useful for your project? In this talk, we will illustrate the various methods and models of Graph Data Science being applied to Knowledge Graphs and how they allow you to find implicit relationships in your graph which are impossible to detect in any other way. You will learn how graph algorithms from PageRank to Embeddings drive ever deeper insights in your data.
Introduction to Neo4j for the Emirates & BahrainNeo4j
This document provides an agenda and overview of a Neo4j presentation. It discusses Neo4j as the leading native graph database, its graph data science capabilities, and deployment options like Neo4j Aura and Cloud Managed Services. Success stories are highlighted like Minka using Neo4j Aura to power Colombia's new real-time ACH payments system. The presentation aims to demonstrate Neo4j's technology, use cases, and how it can drive business value through connecting data.
Neo4j GraphTalk Oslo - Introduction to GraphsNeo4j
The document provides an agenda for an event taking place in Oslo on Tuesday, May 28th 2019. The agenda includes breakfast networking from 9:00-9:30, presentations from 9:30-12:30 on Neo4j and using graphs for various applications, and a Q&A session from 12:30. The document also provides background information on Neo4j, how it can be used to store and query graph data, and various customer examples.
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- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on automated letter generation for Bonterra Impact Management using Google Workspace or Microsoft 365.
Interested in deploying letter generation automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...Tatiana Kojar
Skybuffer AI, built on the robust SAP Business Technology Platform (SAP BTP), is the latest and most advanced version of our AI development, reaffirming our commitment to delivering top-tier AI solutions. Skybuffer AI harnesses all the innovative capabilities of the SAP BTP in the AI domain, from Conversational AI to cutting-edge Generative AI and Retrieval-Augmented Generation (RAG). It also helps SAP customers safeguard their investments into SAP Conversational AI and ensure a seamless, one-click transition to SAP Business AI.
With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
7. Neo4j, Inc. All rights reserved 2021
“By 2025, graph technologies will be
used in 80% of data and analytics
innovations...”
Top 10 Trends in Data and Analytics, 11 May 2020, Rita Sallam et al.