AWS Machine Learning Blog

Category: Amazon SageMaker

AWS and NVIDIA launch “Hands-on Machine Learning with Amazon SageMaker and NVIDIA GPUs” on Coursera

AWS and NVIDIA are excited to announce the new Hands-on Machine Learning with Amazon SageMaker and NVIDIA GPUs course. The course has four parts, and is designed to help machine learning (ML) enthusiasts quickly learn how to perform modern ML in the AWS Cloud. Sign up for the course today on Coursera. Machine learning can be complex, […]

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Use integrated explainability tools and improve model quality using Amazon SageMaker Autopilot

Whether you are developing a machine learning (ML) model for reducing operating cost, improving efficiency, or improving customer satisfaction, there are no perfect solutions when it comes to producing an effective model. From an ML development perspective, data scientists typically go through stages of data exploration, feature engineering, model development, and model training and tuning […]

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Deploy fast and scalable AI with NVIDIA Triton Inference Server in Amazon SageMaker

Machine learning (ML) and deep learning (DL) are becoming effective tools for solving diverse computing problems, from image classification in medical diagnosis, conversational AI in chatbots, to recommender systems in ecommerce. However, ML models that have specific latency or high throughput requirements can become prohibitively expensive to run at scale on generic computing infrastructure. To […]

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Bring your own data to classify news with Amazon SageMaker and Hugging Face

The fields of natural language processing (NLP), natural language understanding (NLU), and related branches of machine learning (ML) for text analysis have rapidly evolved to address use cases involving text classification, summarization, translation, and more. State-of-the art, general-purpose architectures such as transformers are making this evolution possible. Looking at text classification in particular, a supervised […]

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Automate model retraining with Amazon SageMaker Pipelines when drift is detected

Training your machine learning (ML) model and serving predictions is usually not the end of the ML project. The accuracy of ML models can deteriorate over time, a phenomenon known as model drift. Many factors can cause model drift, such as changes in model features. The accuracy of ML models can also be affected by […]

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Host RStudio Connect and Package Manager for ML development in RStudio on Amazon SageMaker

Today, we announced RStudio on Amazon SageMaker, the first machine learning (ML) integrated development environment (IDE) in the cloud for data scientists working in R. The open-source language R and its rich ecosystem with more than 18,000 packages has been a top choice for statisticians, quant analysts, data scientists, and ML engineers. RStudio on SageMaker […]

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Get started with RStudio on Amazon SageMaker

Today, we’re excited to announce RStudio on Amazon SageMaker, the industry’s first fully-managed RStudio integrated development environment (IDE) in the cloud. You can now bring the current RStudio licenses and migrate your self-managed RStudio environments to Amazon SageMaker in a few simple steps. RStudio is one of the most popular IDEs among R developers for […]

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How iProperty.com.my accelerates property-based ML model delivery with Amazon SageMaker

This post was created in collaboration with Mohammed Alauddin, Data Engineering and Data Science Regional Manager, and Kamal Hossain, Lead Data Scientist at iProperty.com.my, now part of PropertyGuru Group. iProperty.com.my is the market-leading property portal in Malaysia and is now part of the PropertyGuru Group. iProperty.com.my offers a search experience that enables property seekers to […]

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Enhance your machine learning development by using a modular architecture with Amazon SageMaker projects

One of the main challenges in a machine learning (ML) project implementation is the variety and high number of development artifacts and tools used. This includes code in notebooks, modules for data processing and transformation, environment configuration, inference pipeline, and orchestration code. In production workloads, the ML model created within your development framework is almost […]

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Onboard OneLogin SSO users to Amazon SageMaker Studio

Amazon SageMaker is a fully managed service that provides every machine learning (ML) developer and data scientist the ability to build, train, and deploy ML models at scale. Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for ML. Amazon SageMaker Studio provides all the tools you need to take your models from experimentation […]

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