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The Weather Company enhances MLOps with Amazon SageMaker, AWS CloudFormation, and Amazon CloudWatch

AWS Machine Learning Blog

TWCo data scientists and ML engineers took advantage of automation, detailed experiment tracking, integrated training, and deployment pipelines to help scale MLOps effectively. The Data Quality Check part of the pipeline creates baseline statistics for the monitoring task in the inference pipeline.

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How Axfood enables accelerated machine learning throughout the organization using Amazon SageMaker

AWS Machine Learning Blog

However, there are many clear benefits of modernizing our ML platform and moving to Amazon SageMaker Studio and Amazon SageMaker Pipelines. Monitoring – Continuous surveillance completes checks for drifts related to data quality, model quality, and feature attribution. Workflow B corresponds to model quality drift checks.

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The Age of Health Informatics: Part 1

Heartbeat

The Role of Data Scientists and ML Engineers in Health Informatics At the heart of the Age of Health Informatics are data scientists and ML engineers who play a critical role in harnessing the power of data and developing intelligent algorithms.

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Arize AI on How to apply and use machine learning observability

Snorkel AI

And usually what ends up happening is that some poor data scientist or ML engineer has to manually troubleshoot this in a Jupyter Notebook. So this path on the right side of the production icon is what we’re calling ML observability. We have four pillars that we use when thinking about ML observability.

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Arize AI on How to apply and use machine learning observability

Snorkel AI

And usually what ends up happening is that some poor data scientist or ML engineer has to manually troubleshoot this in a Jupyter Notebook. So this path on the right side of the production icon is what we’re calling ML observability. We have four pillars that we use when thinking about ML observability.

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How Vodafone Uses TensorFlow Data Validation in their Data Contracts to Elevate Data Governance at Scale

TensorFlow

It can also include constraints on the data, such as: Minimum and maximum values for numerical columns Allowed values for categorical columns. Before a model is productionized, the Contract is agreed upon by the stakeholders working on the pipeline, such as the ML Engineers, Data Scientists and Data Owners.

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Importance of Machine Learning Model Retraining in Production

Heartbeat

Once the best model is identified, it is usually deployed in production to make accurate predictions on real-world data (similar to the one on which the model was trained initially). Ideally, the responsibilities of the ML engineering team should be completed once the model is deployed. But this is only sometimes the case.