How to implement end-to-end MLOps pipeline in Vertex AI with monitoring for model drift?

Google Cloud Build with AI Custom ML & MLOps
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Hi all,

I’m building a full end-to-end MLOps pipeline on Vertex AI, including data preprocessing, training, deployment, and model drift monitoring in production.

Specifically, I’m trying to implement:

  1. Continuous feature and data versioning (e.g. via Feature Store + Git/Dataflow);
  2. CI/CD with Vertex AI Pipelines triggering on updated data or retraining needs;
  3. Drift detection and alerting around input feature distributions and performance metrics;
  4. Automated retraining workflows (if drift threshold exceeded).

What best practices or example architectures exist using Vertex AI to cover this? Do official templates include drift monitoring? Would love to see insights from real implementations!
Thanks in advance :blush:

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You can take a look at these links:

MLOps: Continuous delivery and automation pipelines in machine learning | Cloud Architecture Center | Google Cloud

Architecture for MLOps using TensorFlow Extended, Vertex AI Pipelines, and Cloud Build | Cloud Architecture Center | Google Cloud

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Hi a_aleinikov,

You may check the following documentation, which might help you understand how to implement an end-to-end MLOps pipeline in Vertex AI with model drift monitoring:

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