AI as a Service Scaling AI with Ease and Innovation
summary
Shows concrete wins: a name‑matching task over millions of records moves from manual/Excel to an API, a recommendation system prototype fails when rebuilt in‑house then succeeds when replaced by a proven recommendation API, and PDF regression testing is automated via generative models to avoid human error. Recommends assessing data and ROI, piloting with pre‑trained models from AWS/GCP/Azure/IBM or copilot‑style tools, then scaling. Mentions text‑to‑SQL and verticalized offerings.
AI-written summary · about
chapters (9)
- 0:00Introduction to AI journey
- 3:32Human uniqueness and AI
- 7:50AI as a Service explained
- 12:03Advantages of AIaaS
- 15:39Democratizing AI in IT
- 18:51Integrating AI into apps
- 21:01Generative AI in development
- 23:27Getting started with AIaaS
- 26:17Impact on software development
related talks
01h 43mAI as a Service
30mAI and API, made for each other
15mAI as a Service: The Next thing for Big Tech
13mAI as a Strategic Asset: Building Competitive Advantage
18mAPIs: ROI from artificial intelligence
39mResponsible Applied AI at Scale
12mAI as a Force Multiplier for Cloud Native Enterprise Development
01h 16mAI in Software Development: Boosting Efficiency