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Book Overview & Buying
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Table Of Contents
Real-world End to End Machine Learning Ops on Google Cloud
By :
Real-world End to End Machine Learning Ops on Google Cloud
By:
Overview of this book
This course guides learners through practical ML Ops on Google Cloud, from setup to advanced workflow orchestration. It starts with GCP services and ML Ops fundamentals, then covers building CI/CD pipelines using Cloud Build, Artifact Registry, and Cloud Run. Participants deploy scalable, containerized ML models and perform automated testing.
Next, learners use Airflow with Cloud Composer for continuous training, keeping models updated and managing failures with alerts. The course dives into Vertex AI for efficient model training, deployment, registry management, and predictions, supported by CI/CD automation. Advanced topics include Kubeflow Pipelines, hyperparameter tuning, explainability, and model versioning, enabling refined, transparent AI solutions.
The final section covers generative AI and large language models like PaLM 2 on Google Cloud, with hands-on labs and custom deployments. Practical assignments throughout ensure real-world skills to confidently manage end-to-end ML Ops workflows.
Table of Contents (8 chapters)
Introduction & prerequisites
Introduction to ML Ops
CI/CD using GCP CloudBuild, Artifact & Container Registry and CloudRun
Continuous Model Training using Cloud Composer-Airflow
Vertex AI For Data Science & Machine Learning
Vertex AI-Kubeflow Pipelines for ML Workflow Orchestration
Vertex AI-Hyperparameter Tuning Jobs, Explainability AI & Model Versioning
Generative AI on Google Cloud
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