AWS Machine Learning Blog

FEBRUARY 27, 2024

Monitoring – Continuous surveillance completes checks for drifts related to data quality, model quality, and feature attribution. Workflow A corresponds to preprocessing, data quality and feature attribution drift checks, inference, and postprocessing.

Machine Learning 136

Machine Learning DevOps Data Scientist Data Quality 136

AWS Machine Learning Blog

APRIL 21, 2023

If the model performs acceptably according to the evaluation criteria, the pipeline continues with a step to baseline the data using a built-in SageMaker Pipelines step. For the data drift Model Monitor type, the baselining step uses a SageMaker managed container image to generate statistics and constraints based on your training data.

Data Drift 97

Data Drift Metadata Data Quality ML 97

The MLOps Blog

JUNE 27, 2023

Data quality control: Robust dataset labeling and annotation tools incorporate quality control mechanisms such as inter-annotator agreement analysis, review workflows, and data validation checks to ensure the accuracy and reliability of annotations. Data monitoring tools help monitor the quality of the data.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

Towards AI

AUGUST 16, 2023

MLOps is the intersection of Machine Learning, DevOps, and Data Engineering. Monitoring Models in Production There are several types of problems that Machine Learning applications can encounter over time [4]: Data drift: sudden changes in the features values or changes in data distribution.

Machine Learning 98

Machine Learning Software Development Data Drift Data Science 98

DataRobot Blog

FEBRUARY 11, 2022

These agents apply the concept familiar in the DevOps world—to run models in their preferred environments while monitoring all models centrally. DataRobot’s MLOps product offers a host of features designed to transform organizations’ user experience, firstly, through its model-monitoring agents.

Data Drift 52

Data Drift Machine Learning DevOps Data Scientist 52

AWS Machine Learning Blog

APRIL 26, 2023

Ensuring data quality, governance, and security may slow down or stall ML projects. Data science – The heart of ML EBA and focuses on feature engineering, model training, hyperparameter tuning, and model validation. MLOps engineering – Focuses on automating the DevOps pipelines for operationalizing the ML use case.

ML 113

ML Machine Learning Data Science Data Drift 113

AWS Machine Learning Blog

AUGUST 29, 2023

This architecture design represents a multi-account strategy where ML models are built, trained, and registered in a central model registry within a data science development account (which has more controls than a typical application development account). Refer to Operating model for best practices regarding a multi-account strategy for ML.

Data Scientist 91

Data Scientist Data Quality Python ML 91