Amazon SageMaker is the fully managed machine learning platform covering every stage of the ML lifecycle: labeling, data preparation, training through built-in algorithms, BYO frameworks, or JumpStart, hyperparameter tuning, deployment, and monitoring. SageMaker Studio is the integrated IDE for the entire workflow, and inference can run as real-time endpoints, asynchronous inference, batch transform, or serverless endpoints depending on the workload's latency and traffic shape.
Pre-built AI services deliver high-quality models through simple APIs for common tasks. Amazon Rekognition analyzes images and video for object, scene, and text detection, face analysis and comparison, celebrity recognition, liveness checks, and unsafe content moderation including PPE detection, charged per image or per minute of video. Amazon Comprehend applies natural language processing to text, returning sentiment, entities, key phrases, language, syntax, topic models, and custom entity recognition, with a Comprehend Medical variant for clinical text. Amazon Translate delivers neural machine translation across dozens of languages with custom terminology, formality, and profanity masking controls. Amazon Polly turns text into lifelike speech with many voices and languages, supporting Neural TTS for higher quality, SSML markup, and lexicons for custom pronunciation; output formats include MP3, OGG, and PCM. Amazon Lex builds conversational interfaces for chatbots and voice bots using the same automatic speech recognition and natural language understanding that powers Alexa, with Lambda for fulfillment and Cognito for authentication, easily integrating with Amazon Connect and many messaging channels. Amazon Transcribe converts audio to text in real time or batch, with speaker identification, custom vocabulary, and language identification, including Transcribe Medical for clinical conversations and timestamped JSON output. Amazon Textract goes beyond OCR to extract printed text, handwriting, and structured data such as tables and form fields with spatial context, with pre-trained APIs for expense and identity documents.
The IoT services in AWS let fleets of devices connect to the cloud and to each other. AWS IoT Core is the managed cloud service for connecting, managing, and interacting with billions of devices: the Device Gateway ingests traffic over MQTT and HTTPS, the Message Broker handles publish-subscribe delivery, Device Shadow keeps a virtual state for each device that is synchronized even when the device is offline, the Rules Engine routes events to Lambda, Kinesis, SNS, and more, and Device Defender monitors security posture across the fleet. AWS IoT Greengrass extends AWS to edge devices, running Lambda functions and Docker containers on local hardware with offline operation and synchronizing state with the cloud; it supports machine learning inference at the edge by deploying models trained in the cloud and running them locally.