• views: 242
• date: 17 May 2026
• posted by: 0nelove

Deploying machine learning models in production requires robust automation to handle data validation, training, and deployment efficiently.
What you'll learn
Deploying machine learning models in production requires robust automation to handle data validation, training, and deployment efficiently. In this course, (GCP-PMLE) Automating and Orchestrating ML Pipelines, you'll gain the ability to build and orchestrate end-to-end ML workflows on Google Cloud. First, you'll explore how to develop end-to-end pipelines using frameworks like Kubeflow and Vertex AI Pipelines. Next, you'll discover how to automate model retraining and implement continuous integration and continuous delivery (CI/CD) strategies. Finally, you'll learn how to track and audit metadata, ensuring model and data lineage using Vertex ML Metadata. When you're finished with this course, you'll have the skills and knowledge of MLOps needed to automate and orchestrate scalable ML pipelines.



   
   
   

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