The Three Pillars of MLOps

MLOps is a set of practices that aims to build and maintain high-quality ML systems. The three pillars of MLOps are reproducibility, automation, and reliability.

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1. Reproducibility

Reproducibility is the ability to recreate the same results given the same inputs. In the context of ML, this means that you should be able to:

• Reproduce a model: Given the same code, data, and hyperparameters, you should be able to train a model that has the exact same weights.
• Reproduce a prediction: Given the same model and the same input, you should be able to get the exact same prediction.

To achieve reproducibility, you should:

• Version your code, data, and models.
• Use a containerized environment to ensure that your code is always run in the same environment.
• Set a random seed for all your random operations.

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2. Automation

Automation is the process of using tools to perform tasks that would otherwise be done manually. In the context of ML, this means that you should automate:

• Data preprocessing and feature engineering.
• Model training and evaluation.
• Model deployment and monitoring.

To achieve automation, you should:

• Use a pipeline orchestration tool like Vertex AI Pipelines or Cloud Composer.
• Use a CI/CD tool like Cloud Build to automate your build, test, and deployment processes.

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3. Reliability

Reliability is the ability of a system to perform its intended function without failure. In the context of ML, this means that your system should be able to:

• Handle a large volume of requests without crashing.
• Recover from failures quickly and gracefully.
• Provide accurate and consistent predictions.

To achieve reliability, you should:

• Use a managed ML platform like Vertex AI.
• Use a monitoring tool like Vertex AI Model Monitoring to monitor your model's performance and detect any issues.
• Have a plan in place for rolling back to a previous version of your model if necessary.

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Knowledge Check