Neo4j GraphTalk Basel - Building intelligent Software with Graphs
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The document discusses using graphs and Neo4j to build intelligent solutions. It outlines Neo4j's professional services which include training, solution delivery, and packaged services. Typical technical requirements and a methodology for delivering solutions from use case to implementation are presented. Examples of graph-based solutions and how machine learning can be integrated are provided. Finally, a case study of Adobe migrating from Cassandra to Neo4j is summarized, reducing infrastructure costs significantly.
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Neo4j GraphTalk Basel - Building intelligent Software with Graphs
- 1. Building Intelligent Solutions with Graphs Stefan Kolmar VP Field Engineering EMEA & APAC
- 2. • Neo4j Services • Solutions and Managed Services • Adding AI/ML to Solutions • Real world examples and best practices 2 Agenda
- 4. 4 Neo4j Professional Services Training how to efficiently use the technology Build a transport vehicle with (or for) you
- 5. 5 Neo4j PS Professional Services Offer Training & Enablement Solution Delivery and Management Packaged Services Typically 5-25 days Neo4j advises Customer or SI builds 80% of engagements Custom Scoped 50+ days Neo4j delivers Customer or SI supports 20% of engagements
- 7. 7 Packaged Services Project Lifecycle Graph Awareness Technical Assessment Solution Implementation Roll-out / Production Innovation Lab Bootcamp Solution Design Workshop Solution Audit Staff Augmentation Product Training
- 9. - Performance - Agility - Value connections - 360 degree views (customer, product etc) - Finding patterns / anomalies - Sysadmin friendly - Hardware efficiency - Intuitivity 9 Typical Technical Requirements
- 10. 10 From use case to solution delivery Solution accelerators
- 12. 12 What is needed? Solution accelerators SOLUTION (FOUNDATION) FRAMEWORK DELIVERY METHODOLOGY SKILLS & RESOURCES
- 13. Solution (Foundation) Framework Neo4j Graph Platform Recom Framework Custom App Solution Foundation Framework Neo4j Data Orchestrator Framework Neo4j Deployment Framework Neo4j Managed Service Fraud Framework Network Mgmt Framework Custom App Custom App Custom App Custom App Custom App Neo4j Version Management Service
- 14. Solution (Foundation) Framework Neo4j Graph Platform Recom Telco App Solution Foundation Framework Development & Administration Analytics Tooling Graph Analytics Graph Transactions Data Integration Discovery & VisualizationDrivers App App
- 15. Solution Foundation Framework Neo4j Graph Platform Recom Telco App Solution Foundation Framework Development & Administration Analytics Tooling Graph Analytics Graph Transactions Data Integration Discovery & VisualizationDrivers App App API dev 3rd party graph viz Custom dev - graph viz libraries 3rd party analytics Python, Java ML, ... Kettle 3rd party DI/ EAI Docker Kubernetes Git Lineage Kettle GRANDstack Apache Kafka apoc.load.*
- 16. GRANDstack https://grandstack.io/ GraphQL API Layer Apollo Client ReactJS ( ReactGraphVis) VisJS Dashboards Business Logic (JS) Apollo Server Neo4j GraphQL Pluginneo4j-graphql-js
- 18. Solution (Foundation) Framework Neo4j Graph Platform Recom Telco App Solution Foundation Framework App App Availability Demand Framing & Collateral Sales Demo Solution Beta Repeatable Solution Recommendation Framework HCM Framework Privacy Shield Framework Risk Mgmt Framework Fraud Framework C360 Framework CRM Framework Network Mgmt Bill Of Materials
- 20. 20 Managed Solutions Cloud Managed Services Agile Solution Support
- 21. Project definition Solution Design Workshop Deploy Agile Sprints Solution Delivery Methodology 21 Product backlog Backlog Product Increment Main building blocks ● Project definition: clarity about objectives and organisation ● Solution design workshop: requirements and high level design ● Solution Delivery ○ Agile/SCRUM ○ Traditional / Waterfall ● (Regular) Releases / ● Solution support Solution Support Waterfall
- 22. Main artefacts Small Medium Large Scoping SoW ● ● ● Project def Project definition doc ● ● Design Graph model ● ● ● UI design ● Technical Architecture ● ● ● Backlog / Project plan ● ● ● Sprints Sprint backlog ● ● User Stories ● Roll-out User guide ● Ops guide ● ● Support SLA ● Support document ●
- 23. The image cannot be displayed. Your computer may not have enough memory to open the image, or the image may have been corrupted. Restart your computer, and then open the file again. If the red x still appears, you may have to delete the image and then insert it again. Architecture and design Roll-out and deploy Project management Operations Management IntegrationAPILogicModel Skills & Methods 23 Database
- 25. Where AI and ML fit in 25 Development & Administration Analytics Tooling Graph Analytics Graph Transactions Data Integration Discovery & VisualizationDrivers & APIs AI
- 26. Differences between ML and Analytics 26 Machine learning: • Determine domain parameters • Historical-based discoveries • Learn and improve without explicit programming
- 27. Graph analytics: • Uses inherent graph structures • Uncover real-world networks through their connections • Forecast complex network behavior and identify action Differences between ML and Analytics
- 28. Today challenges with Machine Learning: • Doesn’t take multiple relationship hops into account • Takes time to iteratively train a model • Computational inefficiency of connecting data Benefits of Mixing Graph Analytics with ML Graphs bring: • Context to machine learning • Feature filtration • Connected feature extraction
- 29. • Support for many languages (Python, .Net, Java, Go, JavaScript, R, etc.) • Different data integration options • Triggers, event-driven architecture • User-defined functions and procedures Working with your Machine Learning algorithms and Neo4j
- 30. Pathfinding & Search Centrality / Importance Community Detection Similarity & ML Workflow • Parallel Breadth/Depth First Search • Shortest Path • Single-Source Shortest Path • All Pairs Shortest Path • Degree Centrality • Closeness Centrality • Betweenness Centrality • PageRank/Personalized PageRank • Triangle Count • Clustering Coefficients • Connected Components (Union Find) • Strongly Connected Components • Harmonic Closeness Centrality • Dangalchev Closeness Centrality • Wasserman & Faust Closeness Centrality • Approximate Betweenness Centrality • A* Shortest Path • Yen’s K Shortest Path • K-Spanning Tree (MST) • Minimum Spanning Tree • Euclidean Distance • Cosine Similarity • Jaccard Similarity • Label Propagation • Louvain Modularity – 1 Step • Louvain – Multi-Step • Balanced Triad Out of the box Graph Algorithms in Neo4j • Random Walk • One Hot Encoding
- 31. Knowledge graph example: • Using topic finding ML processes (e.g. Latent Dirichlet Allocation) • Feeding the output into a graph database • Search for topics, find related concepts, etc. 31 Graph and Machine Learning Examples Recommendation engine example: • Use ML processes such as collaborative filtering • Enrich graph with the output • Use graph as feedback for future iterations
- 32. Putting it all Together
- 34. Our Neo4j activity implementation has led to a great decrease in complexity, storage, and infrastructure costs. Our full dataset size is now around 40 GB, down from 50 TB of data that we had stored in Cassandra. We’re able to power our entire activity feed infrastructure using a cluster of 3 Neo4j instances, down from 48 Cassandra instances of pretty much equal specs. That has also led to reduced infrastructure costs. Most importantly, it’s been a breeze for our operations staff to manage since the architecture is simple and lean.” David Fox, Adobe, Oct 2018 34 Customer Quote How can Neo4j Services help you to get there?
- 35. Customer Use Case: • Leading online platform to showcase and discover creative work • More than 10 million members • Allows creatives to share their work with millions of daily visitors • Highlights Adobe software used in the creation process • Drives people to the Adobe Creative Cloud • Social platform for discovery, learning, and more 35 Adobe – Project Behance Activity feed: • Mongo DB (2011) - 125 nodes, dataset size of about 20tb (terabytes) • Cassandra (2015) - 48 nodes, dataset size of about 50tb (terabytes) • Neo4j (2018) - 3 nodes, dataset size of 33gb (gigabytes) 5 day BOOTCAMP
- 36. 36 Conclusion • Neo4j PS makes customer projects successful • through enablement • through project / solution delivery • Graph Based Solutions are accelerators for your success • Neo4j is a good foundation for AI and ML • Customer are using Neo4j for their success
- 37. 37 Thank you
- 38. Building Intelligent Solutions with Graphs Stefan Kolmar VP Field Engineering EMEA & APAC