Neo4j GraphTalk Düsseldorf - Building intelligent solutions with Graphs
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This document discusses using graphs and machine learning to build intelligent solutions. It describes Neo4j services including professional services, training, and managed services. It also outlines innovation labs that help generate graph-based use cases. The document reviews using graph analytics and machine learning together, and provides examples of how Neo4j can be leveraged throughout the machine learning life cycle from data integration to model deployment. Real-world customer examples are also presented.
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Neo4j GraphTalk Düsseldorf - Building intelligent solutions with Graphs
- 1. Building Intelligent Solutions with Graphs Kees Vegter Presales Engineer @Neo4j kees@neo4j.com
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- 3. • Neo4j Services • Solutions and Managed Services • Adding AI/ML to Solutions • Real world examples and best practices 3 Agenda
- 5. PROFESSIONAL SERVICES GRAPH ACADEMY SOLUTIONS CUSTOMER SUPPORT ● Packaged Services ● Staff Augmentation ● Project/Solution Delivery ● Class room training ● Online/Virtual training ● Certification ● Innovation Labs ● Solution Workshops ● Solutions Development ● 24x7x365 & KB ● Platinum support ● Cloud Managed Services ● DBaaS (NEW) ● Agile Solution Support Training Enablement Solution Delivery & Management Organization and offerings
- 6. Key entitiesNorth StarOpportunitiesChallenges Identify Target Use Case Generate Data Model Related “Graph Questions” Executive Feedback Presentation Build Prototype/Wireframes Use Case Generation Day 1 Whiteboard Model Day 1 Neo4j Innovation Lab 3.5 Day Innovation Lab Attendees: Neo4j: Lab Leader, UX Designer, Field Engineer Customer (3,5,7): Mix of Business + It + Data Science…
- 7. Executive Feedback Presentation Executive Feedback Presentation Build Prototype/Wireframes Key entitiesNorth StarOpportunitiesChallenges Identify Target Use Case Generate Data Model Related “Graph Questions” Source data to populate model Build QueryImport Data Materialize Model Day 2 Neo4j Innovation Lab 3.5 Day Innovation Lab
- 8. Executive Feedback Presentation Executive Feedback Presentation Build Prototype/Wireframes Key entitiesNorth StarOpportunitiesChallenges Identify Target Use Case Generate Data Model Related “Graph Questions” Source data to populate model Storyboarding/ Mockups Identify Stakeholders / Personas / Synopsis Build QueryImport Data Craft Scenario Day 2 Materialize Model Day 2 Neo4j Innovation Lab 3.5 Day Innovation Lab
- 9. Executive Feedback Presentation Executive Feedback Presentation Build Prototype/Wireframes Key entitiesNorth StarOpportunitiesChallenges Identify Target Use Case Generate Data Model Related “Graph Questions” Source data to populate model Storyboarding/ Mockups Identify Stakeholders / Personas / Synopsis Build QueryImport Data Build Prototype Day 3 Neo4j Innovation Lab 3.5 Day Innovation Lab
- 10. Executive Feedback Presentation Executive Feedback Presentation Build Prototype/Wireframes Key entitiesNorth StarOpportunitiesChallenges Identify Target Use Case Generate Data Model Related “Graph Questions” Source data to populate model Storyboarding/ Mockups Identify Stakeholders / Personas / Synopsis Build QueryImport Data Finalize & Present Day 3.5 Neo4j Innovation Lab 3.5 Day Innovation Lab
- 11. Neo4j Professional Services Training how to efficiently use Build a transport vehicle with (or for) you 11
- 12. 12 Neo4j PS in the real world Solution Delivery and Management • Packaged Services • Typically 5-25 days • Neo4j advises • Customer builds • 80% of projects • Custom Scoped • 50+ man days • Neo4j delivers • Customer supports • 20% of projects
- 13. 13 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
- 15. • Agility -- constantly changing requirements • Intuitiveness – so that everybody in your organization can understand and influence the solution • High Performance to support connected data scenarios • Scalability when traversing large, complex connected datasets • Sysadmin friendliness • Hardware efficiency 15 Neo4j enables Graph Based Solutions with a need for
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- 17. 17 From use case to solution delivery Solution accelerators
- 18. 18 Why Solutions? Accelerate Customer Success Scale Operations Increased Product Maturity
- 19. 19 What is needed? Solution accelerators SOLUTION (FOUNDATION) FRAMEWORK DELIVERY METHODOLOGY SKILLS & RESOURCES
- 20. Solution (Foundation) Framework Neo4j Graph Platform Recommendation Framework Custom App Solution Foundation Framework Neo4j Data Orchestrator Framework Neo4j Deployment Framework Neo4j Managed Service Fraud Framework Network Management Framework Custom App Custom App Custom App Custom App Custom App
- 21. 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
- 22. 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 with graph viz libraries 3rd party analytics Python, Java ML, ... Kettle 3rd party DI/EAI Docker Kubernetes Git Lineage GRANDstack
- 24. GRANDstack https://grandstack.io/ GraphQL API Layer Apollo Client ReactJS ( ReactGraphVis) VisJS Dashboards Business Logic (JS) Apollo Server Neo4j GraphQL Pluginneo4j-graphql-js
- 25. 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
- 26. Network Management (Telco) Graph Transactions Element Manager Geography Service definitions Kettle Customer data ... Dependency Analysis Spatial queries and path exploration Capacity Analysis Fulfillment Assurance > Impact analysis > Event correlation > Root cause analysis Predefined CYPHER queries and API Metadata
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- 30. Where AI and ML fit in 30 Development & Administration Analytics Tooling Graph Analytics Graph Transactions Data Integration Discovery & VisualizationDrivers & APIs AI
- 31. What are we going to look at? • Differences between graph analytics and machine learning • Benefits of mixing graph analytics with machine learning • Where does Neo4j fit in the Machine Learning Project Timeline Machine Learning and Graph Analytics
- 32. Decisions Machine Learning Pipeline Data Records Data Sources Machine Learning
- 33. Graph analytics: • Uses inherent graph structures • Uncover real-world networks through their connections • Forecast complex network behavior and identify action Graph Analytics
- 34. Graph & ML Algorithms in Neo4j+35 neo4j.com/ graph-algorithms- book/ Pathfinding & Search Centrality / Importance Community Detection Link Prediction Finds optimal paths or evaluates route availability and quality Determines the importance of distinct nodes in the network Detects group clustering or partition options Evaluates how alike nodes are Estimates the likelihood of nodes forming a future relationship Similarity
- 35. (Some) 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
- 36. Neo4j has an ‘out of the box’ Graph Algorithms plugin: • Pathfinding and Search • Centrality and Importance • Community Detection • Similarity and Machine Learning Workflow • Link Prediction Many different ways to work with your ML algorithms in Neo4j: • Support for many languages (Python, .Net, Java, Go, Ruby, etc.) • Different data integration options • Triggers, event-driven architecture, user-defined functions 36 Working with Graph Analytics and ML
- 37. Model 37 Working with Graph Analytics and ML example data data + algo results Train – Test results
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- 39. Knowledge graph example: • Using topic finding ML processes e.g. Latent Dirichlet Allocation (LDA) • Feeding the output into a graph database • Search for topics, find related concepts, etc. 39 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
- 40. 40 Where does Neo4j fit in ML Project Timeline Vizualization Storing and Accessing Models Data Analysis Managing Data Sources From: Graph Powered Machine Learning v1: Alessandro Negro (Manning Publications) https://www.manning.com/books/graph-powered-machine-learning • Connected sources of truth • Knowledge Graph • Clean and Merge • Enrich • Feature Selection • Fast access patterns • Graph Algorithms • Graph Accelerated Machine Learning • Network Dynamics • Store and model data • Store and mixing multiple models • Fast Model Access • Data Navigation • Human Brain Analysis • Improved Communication Machine Learning Project Timeline
- 41. Decisions Machine Learning Pipeline Data Records Data Sources Machine Learning
- 42. Machine Learning Pipeline Data Sources Connected Sources of truth
- 43. 43 Real World Examples and Best Practices
- 44. 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 44 Customer Quote How can Neo4j Services help you to get there?
- 45. 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 45 Adobe – Project Behance Activity feed: • Mongo (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 40gb (gigabytes) 5 day Bootcamp
- 46. 46 Large Commercial Bank Customer Journey Innovation Lab Staff Augmentation Campaign Management Innovation Lab Fraud Project 80 person days TBD Another Innovation Lab
- 48. 48 Conclusion • Neo4j Professional Services makes customer projects successful through: • Enablement • Project / solution delivery • Graph Based Solutions as accelerators • Neo4j is the foundation for AI and ML • Customers are using Neo4j to drive success and deliver value