Making AI FaaSt - QCon SF 2018
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The document discusses the advantages of using Function as a Service (FaaS) for rapid AI implementation, highlighting benefits such as faster service creation, cost efficiency, and low maintenance overhead. It emphasizes a paradigm shift in software development towards treating models as functions, enabling quicker deployments and better integration of multiple algorithms. The authors advocate for retooling the industry to adapt to these changes in AI application development.
Making AI FaaSt - QCon SF 2018
- 1. Making AI FaaSt . Dragos D Haut - Principal Engineer @Adobe Akhilesh Kumar - Applied ML Engineer @Adobe
- 2. FaaS Function as A Service
- 5. FaaS Value Props 1. FaaSter to PROTOTYPE services
- 6. FaaS Value Props 1. FaaSter to CREATE services
- 7. FaaS Value Props 1. FaaSter to create services 2. Never pay for Idle
- 8. FaaS Value Props 1. FaaSter to create services 2. Never pay for Idle 3. Low maintenance overhead
- 9. FaaS: Build more, pay less 1. FaaSter to create services 2. Never pay for Idle 3. Low maintenance overhead
- 10. ||
- 11. Evolution of Business Logic
- 14. Multiple regions
- 15. Microservice Cost & COGS
- 16. Microservice Cost vs FaaS Cost
- 17. VS
- 18. How ?
- 19. FaaS has better premises
- 20. FaaS premises Code - a smaller unit to deploy and scale
- 21. FaaS premises Code - a smaller unit to deploy and scale Request based auto-scaling
- 22. Making AI FaaSt
- 25. "With AI, we should look at the programmer more as a teacher, rather than a micro-manager. " — Peter Norvig, Director of Research at Google.
- 26. " We spent the last 40 years building up tools to build programs to deal with text (code) in a good way …"
- 27. "… but right now we are creating models instead of text, and we just don’t have the tools to deal with that. We need to retool the industry." — Peter Norvig, Director of Research at Google.
- 28. "Neural networks are not just another classifier, they represent the beginning of a fundamental shift in how we write software. They are Software 2.0." — (Nov, 2017) - Andrej Karpathy, Director of AI at Tesla
- 29. Building AI apps
- 35. Training vs Inference Learning vs Answering
- 36. Inference Getting a new data sample to infer an answer
- 38. Inference Runs faster than Training Models process one input at a time
- 39. Inference matches the FaaS model Enough code for a function Each function processes one request at a time function (input) { //1. download and cache model //2. return inference(input) }
- 40. Additional FaaS benefits It's FaaSter to deploy the code directly Never pay for idle Low maintenance overhead
- 41. Real apps integrate multiple algorithms into workflows
- 42. Real apps integrate multiple algorithms into workflows reusing existing functions
- 43. Demo • Jupyter Notebook https://github.com/akh64bit/qconsf • AI Composition http://opensource.adobe.com/adobe-sensei-ai-functions/
- 45. Cast • FaaS Platform - Apache OpenWhisk openwhisk.org • Workflow - Apache OpenWhisk Composer github.com/ibm-functions/composer • Editing AI Action - JupyterLab Notebook jupyter.org • Deploying AI Action - JupyterLab Notebook
- 49. Software 2.0 Model + Code
- 50. Software 2.0 JupyterLab - assist in model development Functions - assist in deploying the model
- 51. Software 2.0 ML Engineers collaborate with software developers
- 52. Software 2.0 With FaaS it's easy to deploy a new AI Model-as-a-Function
- 53. Conclusions FaaS platforms are still maturing
- 54. Conclusions FaaS platforms are still maturing It's FaaSter to deploy AI models
- 55. Conclusions FaaS platforms are still maturing It's FaaSter to deploy AI models Build more services, pay less