Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Deploying AI Models: From Research to Real-World Applications

Deploying AI Models: From Research to Real-World Applications

Gutiérrez, Gilbert

Paperback

Series: AI from Scratch: Step-By-Step Guide to Mastering Artificial Intelligence, Book 13

General Computers

ISBN13: 9798310661011
Publisher: Independently Published
Published: Feb 13 2025
Pages: 328
Weight: 1.68
Height: 0.69 Width: 8.50 Depth: 11.00
Language: English
Artificial intelligence is transforming industries, but building an AI model is only half the battle. The real challenge lies in deploying, scaling, and maintaining AI models in real-world applications. Deploying AI Models: From Research to Real-World Applications is a step-by-step guide that helps you transition AI models from the research phase to production-ready systems.

This book, the 13th installment in the AI from Scratch series, focuses on practical AI deployment strategies-from cloud and edge deployment to on-premises solutions, API integration, and MLOps automation. You'll explore how to optimize model performance, manage data pipelines, monitor AI systems, and ensure security and compliance.

Whether you are a machine learning engineer, data scientist, software developer, or AI enthusiast, this book provides the technical insights and real-world case studies needed to successfully deploy AI models at scale.

What You'll Learn

  • AI Model Lifecycle - Understand the transition from research to production
  • Cloud Deployment - Deploy AI models on AWS SageMaker, Google Vertex AI, and Azure ML
  • Edge AI Deployment - Optimize and run AI models on mobile and IoT devices
  • On-Premises AI Deployment - Deploy AI using Kubernetes, Docker, and private servers
  • MLOps & Automation - Implement CI/CD pipelines for continuous model updates
  • Monitoring & Maintenance - Detect model drift, optimize performance, and retrain models
  • Security & Ethics - Address privacy concerns, AI bias, and compliance regulations
  • Industry Case Studies - Learn from AI deployment in healthcare, finance, e-commerce, and more

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