MLOps
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Overview
What is MLOps
Principles
Practices
Collect performance data
Ways of deploying your model
How often do you deploy a model?
Keep a versioned model repository
Measure and proactively evaluate quality of training data
Testing through the ML pipeline
Business impact is more than just accuracy - understand your baseline
Regularly monitor your model in production
Monitor data quality
Automate the model lifecycle
Create a walking skeleton/steel thread
Appropriately optimise models for inference
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Pitfalls (Avoid)
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Practices

Here are the articles in this section:
Collect performance data
Ways of deploying your model
How often do you deploy a model?
Keep a versioned model repository
Measure and proactively evaluate quality of training data
Testing through the ML pipeline
Business impact is more than just accuracy - understand your baseline
Regularly monitor your model in production
Monitor data quality
Automate the model lifecycle
Create a walking skeleton/steel thread
Appropriately optimise models for inference
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Collect performance data
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