Heartbeat

OCTOBER 30, 2023

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.

Machine Learning 52

Machine Learning Data Drift ML Data Scientist 52

AWS Machine Learning Blog

JANUARY 5, 2024

Baseline job data drift: If the trained model passes the validation steps, baseline stats are generated for this trained model version to enable monitoring and the parallel branch steps are run to generate the baseline for the model quality check. Monitoring (data drift) – The data drift branch runs whenever there is a payload present.

Data Science 110

Data Science Data Drift DevOps Auto-complete 110

AWS Machine Learning Blog

FEBRUARY 27, 2024

However, there are many clear benefits of modernizing our ML platform and moving to Amazon SageMaker Studio and Amazon SageMaker Pipelines. In parallel to using data quality drift checks as a proxy for monitoring model degradation, the system also monitors feature attribution drift using the normalized discounted cumulative gain (NDCG) score.

Machine Learning 112

Machine Learning DevOps Data Quality Data Scientist 112

Snorkel AI

JUNE 30, 2023

This could lead to performance drifts. Performance drifts can lead to regression for a slice of customers. And usually what ends up happening is that some poor data scientist or ML engineer has to manually troubleshoot this in a Jupyter Notebook. The second is drift. The first pillar is performance tracing.

Machine Learning 52

Machine Learning ML Data Quality Data Drift 52

Snorkel AI

JUNE 30, 2023

This could lead to performance drifts. Performance drifts can lead to regression for a slice of customers. And usually what ends up happening is that some poor data scientist or ML engineer has to manually troubleshoot this in a Jupyter Notebook. The second is drift. The first pillar is performance tracing.

Machine Learning 52

Machine Learning ML Data Quality Data Drift 52

AWS Machine Learning Blog

NOVEMBER 9, 2023

Building out a machine learning operations (MLOps) platform in the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML) for organizations is essential for seamlessly bridging the gap between data science experimentation and deployment while meeting the requirements around model performance, security, and compliance.

Data Drift 99

Data Drift Auto-complete ML Automation 99

The MLOps Blog

MARCH 15, 2023

For small-scale/low-value deployments, there might not be many items to focus on, but as the scale and reach of deployment go up, data governance becomes crucial. This includes data quality, privacy, and compliance. For an experienced Data Scientist/ML engineer, that shouldn’t come as so much of a problem.

ETL 52

ETL Data Drift Machine Learning ML 52