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Supercharging Self-Service API Integration with AI


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Using an AI-Driven Model for Self-Service API Integration Developers must often build new applications that pull together multiple back-end services delivered via APIs. A new mobile app, for example, may need to connect to backend ERP or CRM systems using a variety of third-party APIs, which are then integrated and delivered as a new API that provides the mobile app interface. By applying machine learning to this process, developers can greatly increase the speed and reduce the complexity of API integration and management, empowering non-developers with the benefits of API integration.

This presentation outlines the challenges of API integration in a modern cloud context and explains how developers can leverage machine learning to speed application development, reduce errors and improve security and compliance.

Published in: Technology
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Supercharging Self-Service API Integration with AI

  1. 1. Supercharging Self-Service API Integration with AI Ravi Dharnikota, Chief Enterprise Architect | September 10, 2018 API World
  2. 2. corporate overview Self-service
  3. 3. corporate overview Self-healing
  4. 4. corporate overview Self-driving
  5. 5. corporate overview Self-learning
  6. 6. corporate overview Autonomous integration
  7. 7. API Integration Self service/ Experience Self Healing/Cloud Architectures Self Learning/AI Autonomous integration The journey to autonomous integration
  8. 8. Current state: API integration is the new development Digital Platform
  9. 9. API based integration architecture Public APIPartner APIPrivate API Internal Channels API Consumer API Platform System of Insight Cloud Integration B2B Integration Engagement APIs (Microservices) Data,ComputeGrid API Provider (SaaS, Partner, Customer) System of Records Integration (ESB/ETL/MFT/ System APIs) iPaaS Process Orchestration Layer Engagement Processes (Process / Functional APIs)
  10. 10. 10 Connecting to the API Integrating with the API Authenticate Error Handling Events & Polling Workflow Orchestration Bulk Custom Objects Versions Lifecycle Learning Map & Transform Testing
  11. 11. corporate overview Self-service
  12. 12. 13 Marketing Dev QA Sup Finance Sales Dev QA Sup Dev QA Sup INTEGRATION PLATFORM SERVICES Self Service AutomationGovernanceDesign Pros: ZERO Lead Time Faster Integration Dev : 1x LOB is autonomous Cons: Guardrails for establishing standards Marketing Finance Sales CENTRALIZED INTEGRATION SERVICES SupportQADev Pros: Integration Domain Expertise Maintain Integration Standards Cons: Longer Lead Time Longer Dev Time : 6x Priority Alignment Issues Centralized model Self service
  13. 13. User experience ● Abstraction of API complexity ● Patterns ● Security(certification/authorization) ● No Management of Infrastructure ● Low code to enable non-developers
  14. 14. corporate overview Self-healing
  15. 15. Cloud native architecture Metadata • Micro-services architecture • Containerized deployment • Elastic
  16. 16. corporate overview Self-learning
  17. 17. AI and ML will be applied to every aspect of computer programming including API Integration.
  18. 18. Artificial intelligence Vision, robotics, machine learning, NLP Machine learning Supervised learning Classification, regression, recommendation Unsupervised learning Clustering, dimensionality reduction, Reinforcement learning Reward maximization, robot navigation AI/ML: a primer
  19. 19. ML success Face recognition Retail Manufacturing Speech & language recognition Energy Self-driving cars Bio/pharma
  20. 20. ML provides an alternative to coding
  21. 21. corporate overview Recommendations
  22. 22. API recommendations with ML • A recommendation model can be used to provide good guesses for API parameters and common API sequences. • API usage and interaction patterns can be learned from examples. Learning new APIs is time consuming. Learning conventions and API interactions is more time consuming. !
  23. 23. Potential for API recommendations Weather Underground (Weather Observation) ◦ "observation_time_rfc822": "Wed, 27 Jun 2018 17:27:13 -0700", Twitter (Tweet) ◦ "created_at": ”Wed Jun 27 17:27:13 +0000 2018" GitHub (Repository) ◦ "created_at": "2018-06-27 T17:27:13Z",
  24. 24. API mapping and integration • required fields and examples • most commonly used fields • mappings from input fields to destination fields • Recommend API interaction patterns Recommend
  25. 25. Machine learning stages Data Collection Collect and prepare data Data Preparation Make sense of data ML Model Training & Testing Use data to answer questions Model Deployment Deploy and operationalize models 26
  26. 26. Data collection ● API documentation ● GitHub and other public repositories ● Platforms for API integration
  27. 27. Metadata Metadata
  28. 28. Data preparation Previous Snap 3 Previous Snap 2 Previous Snap 1 Current Snap Suggested Snap Mapper Copy Mapper Copy Mongo - Find Mapper Copy Mongo - Find Pipeline Execute Mapper Copy Mongo - Find Pipeline Execute Mapper Mapper Copy JSON Formatter Mapper Copy Json Formatter File Writer JSON formatter File Writer CopyMapper MongoDB -Find Execute Mapper
  29. 29. Decision trees – neural networks
  30. 30. Training and testing Segment + user + org + project Neural networks Recommendation model JSON PARSER FILE READER MAPPER
  31. 31. Model deployment architecture User web app Back-end services Storage Storage replica ML trainingS3 file systemML APIs MachineLearning Metadata Analytics
  32. 32. Case study
  33. 33. Lessons learned ● Need lots of Examples ● Data prep ● Choosing and adjusting the right model ● Trial and Error ● Iterative improvement ● ML as API ● AI enables better API’s and API’s enables better AI 34
  34. 34. Applications ● Competitive edge through best of breed applications ● Digital transformation that scales through self-service ● M&A activity integrating different groups with varied skillsets and apps through API’s 35
  35. 35. corporate overview Future
  36. 36. corporate overview Natural language processing 37
  37. 37. Read a file from S3 Filter the age of customers Convert a JSON file in S3 to csv format
  38. 38. Other uses Maintenance Performance Security
  39. 39. 40 Connecting to the API Integrating with the API Authenticate Error Handling Map & Events & Polling Workflow Orchestration Bulk Custom Objects Versions Lifecycle Learning Transform Testing
  40. 40. 41
  41. 41. 42Photo courtesy of ITEC
  42. 42. 43