INDUSTRIALISING MACHINE LEARNING II

We’ve touched upon the importance of data quality, and what we might term sort of a data-centric strategy around machine learning development. Now, what’s your understanding of a machine learning pipeline?

One thing that people need to understand is the concept of model performance model drift and data drift. How does this fit into MLOps and in terms of management of models and drift?

The issue of data drift is so often misunderstood, particularly by business leaders rather than technical people. What is the importance of drift monitoring? And why it’s important in terms of managing your model performance? When when you have models in production?

How should an organization that is thinking about building machine learning applications, reflect on designing a pipeline for a specific industry use case and what are the kind of considerations in designing that pipeline?

What are your last thoughts when considering the issue around machine learning pipelines and data-centric machine learning and what are your recommendations?

Read More About Machine Learning Pipelines

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