Creating an MVP with Oracle
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The document outlines a framework for creating a minimum viable product (MVP) using Oracle's AI/ML platform, emphasizing fast learning, parallel initiatives, and automation. It details steps for app development, integration, and testing, focusing on new app ideas and leveraging modern technologies like blockchain and machine learning. The content highlights the importance of collaboration, iterative prototyping, and data-driven decision-making throughout the MVP development process.
Creating an MVP with Oracle
- 1. Fast Learning and Building Fast Fail MVP Learn andValidate Parallel Learning and Sharing Hard Work with Elements of Fun, Creativity franco.ucci@oracle.com Creating a Minimal Viable Product with Oracle Getting on the Journey with Oracle AI/ML Based Platform 1
- 2. + New App Idea Learning & Validation: Fast Fail MVP (weeks) Parallel Initiatives (2h-2d) Data / Services Access & Execution Experience Outcome Value Tech Capability
- 3. + New App Idea Data / Services Access & Execution Experience Outcome Value Tech Capability New App for Customer Service From Hardwired App Integration to Data Driven API Led On- Prem to SAAS Integration Mobile App with Back End App APIs, Blockchain, AI Based Workflow SAAS containers CI/CD frameworks auto testing APIs metadata git ML AI automation orchestration abstraction Learning & Validation: Fast Fail MVP (weeks) Parallel Initiatives (2h-2d) microservicespolyglot json image processing journey mapping models low code blockchain chatbots AI/ML Robots
- 4. Y1 Outcome Value X Complexity Y2 Tech Capability + New App Idea Data / Services Access & Execution Experience Learning & Validation: Fast Fail MVP (weeks) Parallel Initiatives (2h-2d)
- 5. X Complexity + New App Idea Data / Services Access & Execution Experience Y1 Outcome Value Y2 Tech Capability Learning & Validation: Fast Fail MVP (weeks) Parallel Initiatives (2h-2d)
- 6. X Complexity + New App Idea Data / Services Access & Execution Experience Y1 Outcome Value Y2 Tech Capability Learning & Validation: Fast Fail MVP (weeks) Parallel Initiatives (2h-2d)
- 7. X Complexity Characteristics Security Scale Management Resilience Operations AIMLAutomation + New App Idea Data / Services Access & Execution Experience Y1 Outcome Value Y2 Tech Capability Learning & Validation: Fast Fail MVP (weeks) Parallel Initiatives (2h-2d)
- 8. X Complexity f(n) Characteristics Security Scale Management Resilience Operations AIMLAutomation + New App Idea Data / Services Access & Execution Experience Y1 Outcome Value Y2 Tech Capability Learning & Validation: Fast Fail MVP (weeks) Parallel Initiatives (2h-2d) speed time functionality capability tech/skills learning workshop evaluation
- 9. X Complexity f(n) Characteristics Security Scale Management Resilience Operations AIMLAutomation + New App Idea Data / Services Access & Execution Experience Y1 Outcome Value Y2 Tech Capability APIs K8s APEX ATP Graph Learning & Validation: Fast Fail MVP (weeks) Parallel Initiatives (2h-2d) u Service blockchain data science java python
- 10. X Complexity f(n) Characteristics Security Scale Management Resilience Operations AIMLAutomation + New App Idea Data / Services Access & Execution Experience Y1 Outcome Value Y2 Tech Capability APIs Analytics Process Functions K8s Integration Dig Assist terraform APEX ATP JSON Workshop iterate Learning & Validation: Fast Fail MVP (weeks) Parallel Initiatives (2h-2d) data science u Service nodeJS IOT data
- 11. Image processing f(n) + New App Idea Data/Services Access/Execution Experience API Policies ML based Analytics blockchain K8s AI based ntegration digital assistant (ML) terraform APEX ATP JSON APIs Learning & Validation: Fast Fail MVP (weeks) Recent Experiences u ServicenodeJS Express facial recognition PoliceAgencyMajorBank From Hardwired App Integration to Data Driven API Led On- Prem to SAAS Integration Mobile App with Back End App APIs, Blockchain, AI Based Workflow Iterative prototyping devops APIs device security blockchain Fast validation Cloud integraion APIsApp connectors Auto testing CI / CD 1st Iteration 2 weeks 4 Iterations over 12 weeks 1st Iteration 2 weeks 3 Iterations over 6 weeks Image processing python tensorflow PSFT OCI VM AI based integration app connectors On Prem Agent APEX ATP JSON APIs federation/security integration analytics date based deployment ML based auto testing
- 12. Learning &Validation: Fast Fail MVP (weeks) Candidate AI/MLTechnology Choices Utilize Compute On Demand Collaborative Workflow Model Deployment Access Data and Open Source Tools Open Source Tools / Languages Visualization Version ControlAI and ML PerformanceBusiness InsightSecurity Capability Prebuilt Process Automation Business Accelerators Application Integration Adapters HCM ERPCX Visual App Builder ML Recommendations Integration Cloud Data access Oracle Analytics for Applications Data Science Service* Oracle Analytics Cloud Analytics Automated Data Center Operations and Machine Learning Complete Infrastructure Automation Complete Database Automation ORACLE CLOUD Autonomous Database Autonomous Database Data Safe Data Safe Visual Builder Digital Assistant APEX Blockchain Cloud Low CodeLow Code
- 13. 13 https://www.oracle.com/cloud/arch itecture-center.html Autonomous Database with a private endpoint Deploy a HA Web App
- 15. 15 Confidential – © 2020 Oracle Internal
- 16. Live Labs In Progress Oracle Confidential – Internal/Restricted/Highly Restricted16 https://apex.oraclecorp.com/pls/apex/f?p=LIVELABS:51:3003544082828:::::
- 17. + New App Idea Data/Services Access/Execution Experience Learning & Validation: Fast Fail MVP (weeks) Parallel Initiatives (2h-2d) Outcome Value Tech Capability How to Get Started Plan for Positive Experience, Skilling, Outcome New Mobile App to Back End Apps Automate Integration between back end Apps through change auto testing APIs analytics automation orchestration low code
- 18. + New App Idea Data/Services Access/Execution Experience Learning & Validation: Fast Fail MVP (weeks) Parallel Initiatives (2h-2d) Outcome Value Tech Capability How to Get Started Plan for Positive Experience, Skilling, Outcome New Mobile App to Back End Apps Automate Integration between back end Apps through change 1. Draft UI 2. Storyboard Scenario 3. Identify Data 4. Identify Apps 5. Identify Touch Points 6. End to End Picture Test 7. Vision Video 8. Idea to Operation Timing 9. Key People 10. Why vs DoNothing A auto testing APIs automation orchestration low code analytics
- 19. + New App Idea Data/Services Access/Execution Experience Learning & Validation: Fast Fail MVP (weeks) Parallel Initiatives (2h-2d) Outcome Value Tech Capability How to Get Started Plan for Positive Experience, Skilling, Outcome New Mobile App to Back End Apps Automate Integration between back end Apps through change 1. Draft UI 2. Storyboard Scenario 3. Identify Data 4. Identify Apps 5. Identify Touch Points 6. End to End Picture Test 7. Vision Video 8. Idea to Operation Timing 9. Key People 10. Why vs DoNothing A auto testing APIs automation orchestration low code 1. Provision 2. Data (Model, Test, Load) 3. Build UI or API 4. Test, Share, Iterate 5. Evaluate B 1. One Page Sharing 2. Functional Check to Model 3. Parallelize Initiatives 4. Build Collective IP 5. Aggregate Learnings 6. Evaluate / Package 7. Showcase / Next Steps C analytics
- 20. + New App Idea Data/Services Access/Execution Experience Learning & Validation: Fast Fail MVP (weeks) Parallel Initiatives (2h-2d) Outcome Value Tech Capability How to Get Started Plan for Positive Experience, Skilling, Outcome New Mobile App to Back End Apps Automate Integration between back end Apps through change 1. Draft UI 2. Storyboard Scenario 3. Identify Data 4. Identify Apps 5. Identify Touch Points 6. End to End Picture Test 7. Vision Video 8. Idea to Operation Timing 9. Key People 10. Why vs DoNothing A auto testing APIs automation orchestration low code 1. Provision 2. Data (Model, Test, Load) 3. Build UI or API 4. Test, Share, Iterate 5. Evaluate B 1. One Page Sharing 2. Functional Check to Model 3. Parallelize Initiatives 4. Build Collective IP 5. Aggregate Learnings 6. Evaluate / Package 7. Showcase / Next Steps C Identify Your Outcome Hero Identify your Tech Hero analytics
- 21. Fast Learning and Building Fast Fail MVP Learn andValidate Parallel Learning and Sharing Hard Work with Elements of Fun, Creativity franco.ucci@oracle.com Creating a Minimal Viable Product with Oracle Getting on the Journey with Oracle AI/ML Based Platform 21