Machine Learning in the Cloud
Automate and Scale Machine Learning Pipelines in the CloudBy Lionel Jansen
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.
Audiobook details
GenreTechnology
Length8 hrs 20 mins
Narrated byListen with 1,000+ voices
FormateBook with Audio
LanguageEnglish
Table of contents
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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