Gradient Group – AWS Machine Learning Blog

Get the latest tactics and strategies to build, test, deploy, and scale your AWS machine learning solutions, models, and end-to-end pipelines

  • Drift Monitoring for Machine Learning Models in AWS

    July 10, 2021 by

    We have trained a machine learning model that meets or exceeds performance metrics as a function of business requirements. We have deployed this model to production after converting our Jupyter notebook into a scalable end-to-end training pipeline, including CI/CD and infrastructure-as-code.

  • Continuous Training of Machine Learning Models in Production

    January 1, 2022 by

    Is continuous training (CT) a machine learning operations (MLOps) best practice? It depends on what we mean by CT. Suppose it means continuously invoking training pipelines to ensure models ‘stay fresh’ as new production data lands in the data lake. The training pipeline workflow could be executed automatically on schedule once per month, once per… Read more

  • Unit Testing Data Validation Microservices for Production ML Pipelines

    December 29, 2021 by

    Unit testing is a vital element of production software engineering. After all, how do we know for sure that our code always returns the expected result regardless of input? Unit testing is especially important in production machine learning because model training and pre-processing functions do not always throw exceptions when they should. Instead, the errors… Read more

  • Testing ML Microservices for Production Deployments

    December 21, 2021 by

    How do we ensure machine learning pipeline components produce the exact result we expect, especially prior to production deployments? We could sanity check by inspecting a few output records by hand, but how do we know for sure that all output records are correct every time? This manual, stage 1 automation “ClickOps” approach is not scalable, consistent,… Read more

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