AWS Machine Learning Blog

JANUARY 5, 2024

Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models. Data science and DevOps teams may face challenges managing these isolated tool stacks and systems.

Data Science 133

Data Science Data Drift DevOps Auto-complete 133

AWS Machine Learning Blog

FEBRUARY 27, 2024

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. Pavel Maslov is a Senior DevOps and ML engineer in the Analytic Platforms team.

Machine Learning 137

Machine Learning DevOps Data Scientist Data Quality 137

Towards AI

AUGUST 16, 2023

Michael Dziedzic on Unsplash I am often asked by prospective clients to explain the artificial intelligence (AI) software process, and I have recently been asked by managers with extensive software development and data science experience who wanted to implement MLOps.

Machine Learning 98

Machine Learning Software Development Data Drift Data Science 98

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 121

Data Drift Auto-complete ML Automation 121

The MLOps Blog

JUNE 27, 2023

This includes features for model explainability, fairness assessment, privacy preservation, and compliance tracking. Some popular data quality monitoring and management MLOps tools available for data science and ML teams in 2023 Great Expectations Great Expectations is an open-source library for data quality validation and monitoring.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

AWS Machine Learning Blog

APRIL 26, 2023

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. Monitoring setup (model, data drift). Deploy to production (inference endpoint).

ML 115

ML Machine Learning Data Science Data Drift 115

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. All models built within DataRobot MLOps support ethical AI through configurable bias monitoring and are fully explainable and transparent.

Data Drift 52

Data Drift Machine Learning DevOps Data Scientist 52