Category: Amazon SageMaker

Anomaly detection is the process of identifying items, events, or occurrences that have different characteristics from the majority of the data. It has many applications in various fields, like fraud detection for credit cards, insurance, or healthcare; network intrusion detection for cybersecurity; KPI metrics monitoring for critical systems; and predictive maintenance for in-service equipment. There […]

Last year, AWS announced the general availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). JumpStart hosts 196 computer vision models, 64 natural language processing (NLP) models, 18 pre-built end-to-end solutions, and 19 example notebooks to help you get started with using […]

This post outlines the best practices for provisioning Amazon SageMaker Studio for data science teams and provides reference architectures and AWS CloudFormation templates to help you get started. We use AWS Service Catalog to provision a Studio domain and users. The AWS Service Catalog allows you to provision these centrally without requiring each user to […]

In this post, you learn how to create a MLOps project to automate the deployment of an Amazon SageMaker endpoint with multiple production variants for A/B testing. You also deploy a general purpose API and testing infrastructure that includes a multi-armed bandit experiment framework. This testing infrastructure will automatically optimize traffic to the best-performing model […]

Amazon SageMaker helps data scientists and developers prepare, build, train, and deploy high-quality machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML. SageMaker accelerates innovation within your organization by providing purpose-built tools for every step of ML development, including labeling, data preparation, feature engineering, statistical bias detection, AutoML, […]

Hugging Face is the technology startup, with an active open-source community, that drove the worldwide adoption of transformer-based models thanks to its eponymous Transformers library. Earlier this year, Hugging Face and AWS collaborated to enable you to train and deploy over 10,000 pre-trained models on Amazon SageMaker. For more information on training Hugging Face models […]

Financial institutions invest heavily to automate their decision-making for trading and portfolio management. In the US, the majority of trading volume is generated through algorithmic trading. [1] With cloud computing, vast amounts of historical data can be processed in real time and fed into sophisticated machine learning (ML) models. This allows market participants to discover […]

As the prevalence of machine learning (ML) and artificial intelligence (AI) grows, you need the best mechanisms to aid in the experimentation and development of your algorithms. You might begin with the several built-in algorithms in Amazon SageMaker that simply require you to point the algorithm at your data and start a SageMaker training job. […]

Hyundai Motor Company, headquartered in Seoul, South Korea, is one of the largest car manufacturers in the world. They have been heavily investing human and material resources in the race to develop self-driving cars, also known as autonomous vehicles. One of the algorithms often used in autonomous driving is semantic segmentation, which is a task […]

AWS customers are increasingly using computer vision (CV) models for improved efficiency and an enhanced user experience. For example, a live broadcast of sports can be processed in real time to detect specific events automatically and provide additional insights to viewers at low latency. Inventory inspection at large warehouses capture and process millions of images […]