AWS Machine Learning Blog

JANUARY 26, 2024

Many customers are looking for guidance on how to manage security, privacy, and compliance as they develop generative AI applications. You can also use Amazon SageMaker Model Monitor to evaluate the quality of SageMaker ML models in production, and notify you when there is drift in data quality, model quality, and feature attribution.

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Generative AI LLM ML AI 122

AWS Machine Learning Blog

NOVEMBER 16, 2023

Amazon SageMaker provides purpose-built tools for machine learning operations (MLOps) to help automate and standardize processes across the ML lifecycle. In this post, we describe how Philips partnered with AWS to develop AI ToolSuite—a scalable, secure, and compliant ML platform on SageMaker.

Data Scientist 89

Data Scientist ML Data Science Automation 89

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. Git is a distributed version control system for software development.

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ETL Data Drift Machine Learning ML 52

AWS Machine Learning Blog

MARCH 9, 2023

As machine learning (ML) models have improved, data scientists, ML engineers and researchers have shifted more of their attention to defining and bettering data quality. Applying these techniques allows ML practitioners to reduce the amount of data required to train an ML model.

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ML Engineer Data Scientist Convolutional Neural Networks ML 77

The MLOps Blog

JULY 18, 2023

And even on the operation side of things, is there a separate operations team, and then you have your research or ml engineers doing these pipelines and stuff? Data annotation team: their role is to label some sets of our data on a continuous basis. How do you ensure data quality when building NLP products?

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Conversational AI Natural Language Processing AI AI 52

The MLOps Blog

MARCH 21, 2023

From gathering and processing data to building models through experiments, deploying the best ones, and managing them at scale for continuous value in production—it’s a lot. As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69