AWS whitepapers documentation change
Summary
Updated Amazon Lookout for Vision section to use shortened 'Lookout for Vision' name consistently
Security assessment
Branding/style change with no impact on security documentation or features.
Diff
diff --git a/whitepapers/latest/aws-overview/machine-learning.md b/whitepapers/latest/aws-overview/machine-learning.md index 7ca989945..45be40858 100644 --- a//whitepapers/latest/aws-overview/machine-learning.md +++ b//whitepapers/latest/aws-overview/machine-learning.md @@ -84 +84 @@ Return to [AWS services](./amazon-web-services-cloud-platform.html). -[Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that makes foundational models (FMs) from Amazon and leading AI startups available through an API. With the Amazon Bedrock serverless experience, you can quickly get started, experiment with FMs, privately customize them with your own data, and seamlessly integrate and deploy FMs into your AWS applications. +[Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that makes foundational models (FMs) from Amazon and leading AI companies available through an API. With the Amazon Bedrock serverless experience, you can quickly get started, experiment with FMs, privately customize them with your own data, and seamlessly integrate and deploy FMs into your AWS applications. @@ -86 +86 @@ Return to [AWS services](./amazon-web-services-cloud-platform.html). -You can choose from a variety of foundation models from leading AI companies, such as AI21 Labs, Anthropic, Cohere, Luma, Meta, Mistral AI, and Stability AI. Or you can use the [Amazon Nova foundation models](https://aws.amazon.com/ai/generative-ai/nova/) available exclusively in Amazon Bedrock. +You can choose from a variety of foundation models from leading AI companies, such as AI21 Labs, Anthropic, Cohere, DeepSeek, Luma, Meta, Mistral AI, and Stability AI. Or you can use the [Amazon Nova foundation models](https://aws.amazon.com/ai/generative-ai/nova/) available exclusively in Amazon Bedrock. @@ -146 +146,5 @@ Amazon Lex enables developers to build conversational chatbots quickly. With Ama -[Amazon Lookout for Metrics](https://aws.amazon.com/lookout-for-metrics/) uses ML to automatically detect and diagnose anomalies (outliers from the norm) in business and operational data, such as a sudden dip in sales revenue or customer acquisition rates. In a couple of clicks, you can connect Amazon Lookout for Metrics to popular data stores such as Amazon S3, Amazon Redshift, and Amazon Relational Database Service (Amazon RDS), as well as third-party Software as a Service (SaaS) applications, such as Salesforce, Servicenow, Zendesk, and Marketo, and start monitoring metrics that are important to your business. Amazon Lookout for Metrics automatically inspects and prepares the data from these sources to detect anomalies with greater speed and accuracy than traditional methods used for anomaly detection. You can also provide feedback on detected anomalies to tune the results and improve accuracy over time. Amazon Lookout for Metrics makes it easy to diagnose detected anomalies by grouping together anomalies that are related to the same event and sending an alert that includes a summary of the potential root cause. It also ranks anomalies in order of severity so that you can prioritize your attention to what matters the most to your business. +###### Note + +On October 10, 2025, AWS will discontinue support for Amazon Lookout for Metrics. For more information, see [Transitioning off Amazon Lookout for Metrics](https://aws.amazon.com/blogs/machine-learning/transitioning-off-amazon-lookout-for-metrics/). + +[Amazon Lookout for Metrics](https://aws.amazon.com/lookout-for-metrics/) uses ML to automatically detect and diagnose anomalies (outliers from the norm) in business and operational data, such as a sudden dip in sales revenue or customer acquisition rates. In a couple of clicks, you can connect Amazon Lookout for Metrics to popular data stores such as Amazon S3, Amazon Redshift, and Amazon Relational Database Service (Amazon RDS), as well as third-party Software as a Service (SaaS) applications, such as Salesforce, Servicenow, Zendesk, and Marketo, and start monitoring metrics that are important to your business. Lookout for Metrics automatically inspects and prepares the data from these sources to detect anomalies with greater speed and accuracy than traditional methods used for anomaly detection. You can also provide feedback on detected anomalies to tune the results and improve accuracy over time. Lookout for Metrics makes it easy to diagnose detected anomalies by grouping together anomalies that are related to the same event and sending an alert that includes a summary of the potential root cause. It also ranks anomalies in order of severity so that you can prioritize your attention to what matters the most to your business. @@ -150 +154 @@ Amazon Lex enables developers to build conversational chatbots quickly. With Ama -[Amazon Lookout for Vision](https://aws.amazon.com/lookout-for-vision/) is an ML service that spots defects and anomalies in visual representations using computer vision (CV). With Amazon Lookout for Vision, manufacturing companies can increase quality and reduce operational costs by quickly identifying differences in images of objects at scale. For example, Amazon Lookout for Vision can be used to identify missing components in products, damage to vehicles or structures, irregularities in production lines, miniscule defects in silicon wafers, and other similar problems. Amazon Lookout for Vision uses ML to see and understand images from any camera as a person would, but with an even higher degree of accuracy and at a much larger scale. Amazon Lookout for Vision allows customers to eliminate the need for costly and inconsistent manual inspection, while improving quality control, defect and damage assessment, and compliance. In minutes, you can begin using Amazon Lookout for Vision to automate inspection of images and objects – with no ML expertise required. +[Amazon Lookout for Vision](https://aws.amazon.com/lookout-for-vision/) is an ML service that spots defects and anomalies in visual representations using computer vision (CV). With Amazon Lookout for Vision, manufacturing companies can increase quality and reduce operational costs by quickly identifying differences in images of objects at scale. For example, Lookout for Vision can be used to identify missing components in products, damage to vehicles or structures, irregularities in production lines, miniscule defects in silicon wafers, and other similar problems. Amazon Lookout for Vision uses ML to see and understand images from any camera as a person would, but with an even higher degree of accuracy and at a much larger scale. Lookout for Vision allows customers to eliminate the need for costly and inconsistent manual inspection, while improving quality control, defect and damage assessment, and compliance. In minutes, you can begin using Lookout for Vision to automate inspection of images and objects – with no ML expertise required. @@ -276 +280 @@ Amazon SageMaker AI makes it easy to [deploy ML models](https://aws.amazon.com/s -[Apache MXNet](https://mxnet.apache.org/versions/1.9.1/) is a fast and scalable training and inference framework with an easy-to-use, concise [API for ML](https://aws.amazon.com/mxnet/). MXNet includes the [Gluon](https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/index.html) interface that allows developers of all skill levels to get started with deep learning on the cloud, on edge devices, and on mobile apps. In just a few lines of Gluon code, you can build linear regression, convolutional networks and recurrent LSTMs for object detection, speech recognition, recommendation, and personalization. You can get started with MxNet on AWS with a fully managed experience using [Amazon SageMaker AI](https://aws.amazon.com/sagemaker/), a platform to build, train, and deploy ML models at scale. Or, you can use the [AWS Deep Learning AMIss](https://aws.amazon.com/machine-learning/amis/) to build custom environments and workflows with MxNet as well as other frameworks including [TensorFlow](https://aws.amazon.com/tensorflow/), PyTorch, Chainer, Keras, Caffe, Caffe2, and Microsoft Cognitive Toolkit. +[Apache MXNet](https://mxnet.apache.org/versions/1.9.1/) is a fast and scalable training and inference framework with an easy-to-use, concise [API for ML](https://aws.amazon.com/mxnet/). MXNet includes the [Gluon](https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/index.html) interface that allows developers of all skill levels to get started with deep learning on the cloud, on edge devices, and on mobile apps. In just a few lines of Gluon code, you can build linear regression, convolutional networks and recurrent LSTMs for object detection, speech recognition, recommendation, and personalization. You can get started with MxNet on AWS with a fully managed experience using [Amazon SageMaker AI](https://aws.amazon.com/sagemaker/), a platform to build, train, and deploy ML models at scale. Or, you can use the [AWS Deep Learning AMIs](https://aws.amazon.com/machine-learning/amis/) to build custom environments and workflows with MxNet as well as other frameworks including [TensorFlow](https://aws.amazon.com/tensorflow/), PyTorch, Chainer, Keras, Caffe, Caffe2, and Microsoft Cognitive Toolkit. @@ -278 +282 @@ Amazon SageMaker AI makes it easy to [deploy ML models](https://aws.amazon.com/s -### AWS Deep Learning AMIss +### AWS Deep Learning AMIs @@ -296 +300 @@ With [Hugging Face on Amazon SageMaker AI](https://aws.amazon.com/machine-learni -[PyTorch](https://aws.amazon.com/pytorch/) is an open-source deep learning framework that makes it easy to develop machine learning models and deploy them to production. Using [TorchServe](https://docs.aws.amazon.com/dlami/latest/devguide/tutorial-torchserve.html), PyTorch's model serving library built and maintained by AWS in partnership with Facebook, PyTorch developers can quickly and easily deploy models to production. PyTorch also provides dynamic computation graphs and libraries for distributed training, which are tuned for high performance on AWS. You can get started with PyTorch on AWS using [Amazon SageMaker](https://aws.amazon.com/sagemaker/), a fully managed ML service that makes it easy and cost-effective to build, train, and deploy PyTorch models at scale. If you prefer to manage the infrastructure yourself, you can use the [AWS Deep Learning AMIss](https://aws.amazon.com/machine-learning/amis/) or the [AWS Deep Learning Containers](https://aws.amazon.com/machine-learning/containers/), which come built from source and optimized for performance with the latest version of PyTorch to quickly deploy custom machine learning environments. +[PyTorch](https://aws.amazon.com/pytorch/) is an open-source deep learning framework that makes it easy to develop machine learning models and deploy them to production. Using [TorchServe](https://docs.aws.amazon.com/dlami/latest/devguide/tutorial-torchserve.html), PyTorch's model serving library built and maintained by AWS in partnership with Facebook, PyTorch developers can quickly and easily deploy models to production. PyTorch also provides dynamic computation graphs and libraries for distributed training, which are tuned for high performance on AWS. You can get started with PyTorch on AWS using [Amazon SageMaker](https://aws.amazon.com/sagemaker/), a fully managed ML service that makes it easy and cost-effective to build, train, and deploy PyTorch models at scale. If you prefer to manage the infrastructure yourself, you can use the [AWS Deep Learning AMIs](https://aws.amazon.com/machine-learning/amis/) or the [AWS Deep Learning Containers](https://aws.amazon.com/machine-learning/containers/), which come built from source and optimized for performance with the latest version of PyTorch to quickly deploy custom machine learning environments. @@ -300 +304 @@ With [Hugging Face on Amazon SageMaker AI](https://aws.amazon.com/machine-learni -[TensorFlow](https://aws.amazon.com/tensorflow/) is one of many deep learning frameworks available to researchers and developers to enhance their applications with machine learning. AWS provides broad support for TensorFlow, enabling customers to develop and serve their own models across computer vision, natural language processing, speech translation, and more. You can get started with TensorFlow on AWS using [Amazon SageMaker AI](https://aws.amazon.com/sagemaker/), a fully managed ML service that makes it easy and cost-effective to build, train, and deploy TensorFlow models at scale. If you prefer to manage the infrastructure yourself, you can use the [AWS Deep Learning AMIss](https://aws.amazon.com/machine-learning/amis/) or the [AWS Deep Learning Containers](https://aws.amazon.com/machine-learning/containers/), which come built from source and optimized for performance with the latest version of TensorFlow to quickly deploy custom ML environments. +[TensorFlow](https://aws.amazon.com/tensorflow/) is one of many deep learning frameworks available to researchers and developers to enhance their applications with machine learning. AWS provides broad support for TensorFlow, enabling customers to develop and serve their own models across computer vision, natural language processing, speech translation, and more. You can get started with TensorFlow on AWS using [Amazon SageMaker AI](https://aws.amazon.com/sagemaker/), a fully managed ML service that makes it easy and cost-effective to build, train, and deploy TensorFlow models at scale. If you prefer to manage the infrastructure yourself, you can use the [AWS Deep Learning AMIs](https://aws.amazon.com/machine-learning/amis/) or the [AWS Deep Learning Containers](https://aws.amazon.com/machine-learning/containers/), which come built from source and optimized for performance with the latest version of TensorFlow to quickly deploy custom ML environments.