AI Engineering: Building Applications with Foundation Models

Machine Learning Updated Apr 12, 2026

🔄 Updated Apr 2026 🖥️ Self-hostable

Overview

AI Engineering: Building Applications with Foundation Models is a book by Chip Huyen that serves as a canonical reference for productionizing foundation-model apps. It provides guidance on building applications with foundation models, covering topics such as model development, deployment, and maintenance. The book is aimed at helping developers and engineers create effective AI applications.

Problem It Solves

Productionizing foundation models for real-world applications

Target Audience: AI and machine learning engineers, developers, and researchers

Inputs

• Foundation model architectures
• Training data
• Application requirements
• Deployment environments

Outputs

• Trained models
• Deployed applications
• Model evaluation metrics
• Application performance metrics

Example Workflow

1 Model selection and design
2 Data preparation and preprocessing
3 Model training and fine-tuning
4 Model deployment and maintenance
5 Application monitoring and evaluation
6 Continuous model improvement

Sample System Prompt


              Design and deploy a sentiment analysis application using a foundation model

Tools & Technologies

Hugging Face Transformers TensorFlow PyTorch AWS SageMaker

Alternatives

• Designing Machine Learning Systems
• Machine Learning Engineering
• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

FAQs

Is this agent open-source?: No
Can this agent be self-hosted?: Yes
What skill level is required?: Intermediate

Rate This Agent

Your rating:

Reviews

Loading reviews...