Technology

Machine Learning in the Cloud

Automate and Scale Machine Learning Pipelines in the CloudBy Lionel Jansen

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Length8h 20m

About this audiobook

Are you ready to deploy and scale machine learning models in the cloud with confidence? Machine Learning in the Cloud is your hands-on guide to production ML, covering managed services, pipelines, monitoring, and cloud architecture. From beginner to pro, this book teaches you to build, deploy, and monitor ML models using AWS SageMaker, Google AI Platform, and Azure ML. Learn MLOps, CI/CD for ML, model versioning, and cost optimization. With real-world examples and step-by-step tutorials, you'll master scalable ML deployment without the hype. Whether you're a data scientist or DevOps engineer, this book bridges the gap between experimentation and production. Start your journey to cloud ML mastery today! What You'll Learn Deploy models on AWS, GCP, and Azure Automate ML pipelines with Kubeflow and Airflow Monitor model drift and retraining Optimize costs for serverless inference Implement CI/CD for machine learning This book stands out from competitors like [placeholder] and [placeholder] by focusing on practical, cloud-agnostic strategies that work in any environment. Perfect for engineers who want to go from zero to production-ready ML systems. This hands-on Cloud/DevOps guide is written to be used at the keyboard: every concept is paired with something you can run, adapt, and keep. You move from first principles to real, working results, with the common errors and fixes called out along the way so you are never stuck for long.

Artificial Intelligence

Audiobook details

GenreTechnology

Length8 hrs 20 mins

Narrated byListen with 1,000+ voices

FormateBook with Audio

LanguageEnglish

1Preface

2Chapter 1 — Infrastructure as Code: Provision Your Cloud ML Environment with Terraform

3Chapter 2 — Data Lakes and Storage Strategies: Ingest and Version Datasets at Scale

4Chapter 3 — Serverless Data Processing: Clean and Transform Gigabyte-Scale Datasets

5Chapter 4 — Training at Scale: Launch GPU-Enhanced Training Jobs with Managed Services

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6Chapter 5 — Hyperparameter Optimization: Automate Model Tuning with Bayesian Search

7Chapter 6 — Model Registry and Version Control: Track Experiments and Promote Models

8Chapter 7 — Containerized Inference: Package and Deploy Models as REST APIs

9Chapter 8 — Serverless Inference: Score Millions of Transactions Overnight

10Chapter 9 — CI/CD Pipelines for ML: Automate Training, Testing, and Deployment

11Chapter 10 — Monitoring and Observability: Track Data Drift, Model Performance, and System Health

12Chapter 11 — Self-Healing Pipelines: Automatically Retrain When Drift Is Detected

13Chapter 12 — Cost Optimization: Right-Size Resources and Slash Unnecessary Spending

14Chapter 13 — Security and Compliance: Lock Down Access with Least-Privilege Policies

15Chapter 14 — Infrastructure as Test-Driven Deployment: Validate Changes with Policy as Code

16Chapter 15 — Multi-Cloud Strategy: Deploy the Fraud Model Simultaneously to AWS and Azure

17Chapter 16 — Kubernetes Anywhere: Orchestrate Model Serving with EKS and AKS

18Chapter 17 — Advanced MLOps: Feature Stores, Pipelines as Code, and Canary Deployments

19Chapter 18 — The Complete System: Bringing It All Together with a Single CI/CD Pipeline

20About the Author

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Machine Learning in the Cloud

About this audiobook

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