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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Data Scientist ML Engineer Data Science Machine Learning 110

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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ML Machine Learning Auto-complete Auto-classification 89

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 88

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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Data Scientist ML Data Science Machine Learning 108

Pickl AI

NOVEMBER 28, 2024

Fundamental Programming Skills Strong programming skills are essential for success in ML. This section will highlight the critical programming languages and concepts ML engineers should master, including Python, R , and C++, and an understanding of data structures and algorithms. during the forecast period.

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Machine Learning Neural Network ML Engineer Algorithm 52

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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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 Machine Learning AI 52