# Artificial Intelligence as a Service with Peter Elger and Eóin Shanaghy - ML 179

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Text version: https://stenobird.com/podcast/adventures-in-machine-learning/artificial-intelligence-as-a-service-with-peter-elger-and-e-in-shanaghy-ml-179.md
Podcast: [Adventures in Machine Learning](https://stenobird.com/podcast/adventures-in-machine-learning)
Published: 2024-12-19T11:00:00+00:00
Episode link: https://www.spreaker.com/episode/artificial-intelligence-as-a-service-with-peter-elger-and-eoin-shanaghy-ml-179--63399956
Audio file: https://dts.podtrac.com/redirect.mp3/api.spreaker.com/download/episode/63399956/ml_179.mp3
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Duration seconds: 3295

## Resource

Leverage pre-built cloud models to implement machine learning without needing deep mathematical expertise. The authors explain how AWS services allow developers to integrate advanced AI capabilities via simple API calls.

## Highlights
- Main idea: AI as a Service allows developers to use powerful, pre-trained algorithms without being math experts
- Practical takeaway: Use AWS Lambda with Docker images to deploy large machine learning models up to 10GB
- Practical takeaway: API-based integration enables rapid implementation of image recognition and text analysis
- Failure mode: Relying solely on infrastructure scaling without monitoring costs can lead to unexpected expenses
- Main idea: The shift toward containerized serverless functions is a game changer for ML inference

## Topics

Artificial Intelligence, Machine Learning, AWS Lambda, Cloud Computing, Docker, Model Deployment, API Integration, Serverless Architecture

## Chapters
- 1:10 — Introduction to AI as a Service: Introduction to the authors of 'AI as a Service' and their expertise in AWS consulting.
- 5:40 — The Efficiency of Pre-trained Models: Why using existing models from providers like Amazon, Google, and Microsoft is more cost-effective than building from scratch.
- 10:00 — Human-in-the-loop Systems: Discussing confidence intervals and when to route AI results to human reviewers.
- 14:20 — Implementing ML with JavaScript: The accessibility of AI via API calls and the choice of JavaScript for ML implementation.
- 18:45 — Request-Response Patterns in AI: How to handle image recognition and other asynchronous AI tasks in a cloud environment.
- 23:15 — Real-world Use Case: KYC: A look at using automated scanning for Know Your Customer (KYC) compliance.
- 27:35 — The Impact of Large Container Images: How 10GB Docker images on AWS Lambda enable complex model inference without specialized ML services.

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## Transcript

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