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Introduction

Part 4 mission patch

Learn how to monitor and maintain the model using Fluent Bit Fluent Bit and Evidently AI.

Requirements

The following requirements are the same as those described in the third part:

Using different platforms? Read this!

While this guide uses GitHub and Google Cloud for examples, the core MLOps principles and architecture patterns apply to other platforms with some adjustments:

  • Development platforms: Works with on-premise solutions like GitLab (comprehensive built-in CI/CD system) and Gitea (largely compatible with GitHub Actions syntax).
  • Cloud providers: Adaptable to other cloud platforms with appropriate service mappings.

Note

A credit card might be necessary to use cloud services.

Before proceeding with this section, please ensure that you have a valid payment method, as it may be required to utilize cloud services. It is important to note that at the conclusion of this section, you will need to delete the cloud resources that were created to avoid any potential charges.

While the costs associated with this section are expected to be free, it is recommended to review the pricing details of cloud services before initiating this part.

State of the MLOps process

Once the model is deployed, it needs to be observed and maintained in production. In this part, you will address the following issues:

  • Model predictions cannot be monitored in production
  • Data drift and concept drift are not monitored
  • No automated reports or dashboard are configured
  • Drift signals do not trigger actionable alerts
  • Drift alerts do not lead to a reviewed decision