AWS Machine Learning Blog

JULY 8, 2024

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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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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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.

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AWS Machine Learning Blog

NOVEMBER 14, 2024

Model governance involves overseeing the development, deployment, and maintenance of ML models to help ensure that they meet business objectives and are accurate, fair, and compliant with regulations. It also helps achieve data, project, and team isolation while supporting software development lifecycle best practices.

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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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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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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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