Machine Learning Related Services

1. Amazon SageMaker AI

Amazon SageMaker AI is a fully managed machine learning service that helps developers, data scientists, and businesses build, train, and deploy machine learning models at scale. It provides tools for every step of the ML workflow, including data preparation, model training, tuning, evaluation, and deployment. SageMaker supports popular frameworks like TensorFlow, PyTorch, and Scikit-learn, and also offers built-in algorithms for common use cases. It is widely used for applications such as predictive analytics, fraud detection, recommendation systems, and natural language processing. AWS manages the underlying infrastructure so users can focus on building models instead of managing servers.

Example:
A retail company can use Amazon SageMaker AI to build a recommendation system that suggests products to customers based on their browsing and purchase history.

2. Amazon Augmented AI

Amazon Augmented AI is a service that helps improve the accuracy of machine learning predictions by adding human review to AI outputs. It is designed for “human-in-the-loop” workflows, where AI models make predictions but humans step in to review low-confidence or critical cases. This helps businesses build more reliable AI systems without fully relying on automation. Amazon Augmented AI integrates with services like Amazon SageMaker and Amazon Rekognition to add human validation for tasks such as image analysis, text extraction, and content moderation. AWS manages the workflow, routing, and scaling of human review tasks.

Example:
A bank can use Amazon Augmented AI to automatically flag suspicious transactions using AI, while sending unclear or high-risk cases to human reviewers for final verification.

3. Amazon CodeGuru

Amazon CodeGuru is a developer tool that uses machine learning to automatically review code and identify issues in applications. It helps improve code quality by detecting bugs, security vulnerabilities, performance bottlenecks, and inefficient coding patterns. CodeGuru provides recommendations to fix problems and optimize application performance. It also includes profiling capabilities that analyze running applications to find expensive lines of code. It integrates with CI/CD pipelines and repositories like CodeCommit and GitHub. The service is commonly used to improve software reliability, security, and performance during development.

Example:
A development team can use Amazon CodeGuru to automatically scan their Java application and get suggestions to fix memory leaks and improve runtime performance before deploying it to production.

4. Amazon DevOps Guru

Amazon DevOps Guru is a machine learning–powered service that helps developers and operations teams detect, diagnose, and resolve operational issues in applications. It analyzes application metrics, logs, and events from services like Amazon CloudWatch and AWS X-Ray to identify abnormal behavior such as performance degradation, errors, or resource bottlenecks. DevOps Guru automatically alerts users when it detects potential problems and provides insights and recommendations to fix them quickly. It is designed to reduce downtime and improve application reliability by proactively finding issues before they impact users. The service is commonly used in cloud-native and microservices-based architectures.

Example:
A company running an e-commerce website can use Amazon DevOps Guru to detect a sudden spike in API latency and get recommendations on how to fix the underlying issue before customers are affected.

5. Amazon Comprehend

Amazon Comprehend is a fully managed natural language processing (NLP) service that uses machine learning to analyze and extract insights from text data. It can identify key phrases, sentiment (positive, negative, neutral), entities (like names, places, organizations), language, and topics from large volumes of text. Amazon Comprehend helps businesses understand customer feedback, social media content, documents, and support tickets at scale. It is commonly used for sentiment analysis, content categorization, and text mining. AWS manages the underlying ML infrastructure so users do not need to build or train NLP models themselves.

Example:
A company can use Amazon Comprehend to analyze thousands of customer reviews and automatically determine whether most feedback about a product is positive or negative.

6. Amazon Forecast

Amazon Forecast is a fully managed machine learning service that helps businesses predict future outcomes based on historical data. It is designed for time-series forecasting, which means it analyzes past patterns to forecast things like sales, demand, inventory, traffic, and resource usage. Amazon Forecast automatically builds and trains machine learning models without requiring users to have deep ML expertise. It combines statistical and machine learning techniques to improve prediction accuracy. The service is commonly used in retail, finance, supply chain, and workforce planning to make better business decisions.

Example:
A retail company can use Amazon Forecast to predict future product demand during festival seasons so it can stock the right amount of inventory and avoid shortages or overstocking.

7. Amazon Fraud Detector

Amazon Fraud Detector is a fully managed machine learning service that helps businesses identify potentially fraudulent online activities in real time. It analyzes user behavior and transaction data to detect patterns associated with fraud, such as fake account creation, payment fraud, or suspicious login attempts. Amazon Fraud Detector uses pre-built ML models and also allows custom models tailored to specific business needs. It is commonly used in industries like e-commerce, banking, gaming, and online services to reduce financial losses and improve security. AWS handles model training, scaling, and infrastructure automatically.

Example:
An online marketplace can use Amazon Fraud Detector to flag suspicious orders where a new account suddenly places a high-value purchase using a risky payment method.

8. Amazon Kendra

Amazon Kendra is a fully managed enterprise search service that uses natural language processing and machine learning to help users find information across large collections of documents and data sources. It allows employees or customers to search for answers in systems like file storage, databases, websites, and internal knowledge bases using simple natural language queries instead of complex keywords. Amazon Kendra ranks and returns the most relevant answers, often extracting direct responses from documents. It is commonly used in enterprise knowledge management, customer support, and internal documentation search systems. AWS handles indexing, scaling, and ML-based ranking automatically.

Example:
A company can use Amazon Kendra to let employees quickly search internal policies and documents by asking questions like “What is the company leave policy?” and get instant answers.

9. Amazon Personalize

Amazon Personalize is a fully managed machine learning service that helps businesses build real-time personalized recommendation systems for their users. It analyzes user behavior such as clicks, purchases, and browsing history to deliver customized product, content, or item recommendations. Amazon Personalize uses machine learning models similar to those used by Amazon.com to generate highly relevant suggestions without requiring users to build ML systems from scratch. It is commonly used in e-commerce, media streaming, advertising, and content platforms to improve user engagement and sales. AWS manages data processing, model training, and scaling automatically.

Example:
A streaming platform can use Amazon Personalize to recommend movies and shows to users based on what they have previously watched and liked.

10. Amazon Polly

Amazon Polly is a fully managed service that converts written text into natural-sounding speech using advanced deep learning technologies. It supports multiple languages, voices, and speech styles, allowing applications to create lifelike audio from text. Amazon Polly is commonly used in voice-enabled applications, audiobooks, e-learning platforms, and accessibility tools for visually impaired users. It can also stream speech in real time or store it as audio files like MP3 or OGG. AWS handles voice synthesis, scaling, and infrastructure automatically.

Example:
A news app can use Amazon Polly to convert written news articles into audio so users can listen to them like a podcast while traveling.

11. Amazon Rekognition

Amazon Rekognition is a fully managed computer vision service that uses machine learning to analyze images and videos. It can detect objects, people, faces, text, scenes, and even identify inappropriate or unsafe content. Amazon Rekognition also supports facial recognition, allowing applications to compare faces, verify identities, and track individuals across images or video streams. It is commonly used in security systems, media analysis, user verification, and content moderation. AWS manages all underlying ML models and infrastructure, so developers can easily add visual intelligence to their applications.

Example:
A security company can use Amazon Rekognition to automatically detect unauthorized people in CCTV footage and send alerts when a known threat is identified.

12. Amazon Textract

Amazon Textract is a fully managed machine learning service that automatically extracts text, handwriting, tables, and structured data from scanned documents and images. Unlike simple OCR tools, Amazon Textract understands the layout of documents, so it can identify key-value pairs, forms, invoices, and tables without manual configuration. It is commonly used to digitize paperwork, automate data entry, and process business documents such as invoices, receipts, and forms. AWS handles all the machine learning and scaling, so developers can easily integrate document analysis into applications.

Example:
A finance company can use Amazon Textract to automatically extract invoice details like vendor name, amount, and date from scanned bills and directly input them into their accounting system.

13. Amazon Transcribe

Amazon Transcribe is a fully managed automatic speech recognition (ASR) service that converts spoken language into written text. It uses machine learning to accurately transcribe audio from sources like phone calls, videos, meetings, and live streams. Amazon Transcribe supports multiple languages and can also identify different speakers in a conversation, add timestamps, and filter out unwanted words. It is commonly used for call center analytics, meeting transcription, subtitle generation, and voice-enabled applications. AWS manages the underlying ML models, scaling, and processing infrastructure automatically.

Example:
A customer support center can use Amazon Transcribe to convert recorded phone calls into text so they can analyze customer complaints and improve service quality.

14. Amazon Translate

Amazon Translate is a fully managed neural machine translation service that automatically translates text from one language to another using deep learning models. It supports many global languages and is designed to deliver natural, fluent translations at scale. Amazon Translate can be used for real-time translation or batch processing of large documents. It is commonly used in multilingual websites, customer support systems, chat applications, and content localization. AWS handles all model training, scaling, and infrastructure management.

Example:
An e-commerce website can use Amazon Translate to automatically translate product descriptions and customer reviews into multiple languages so users from different countries can easily understand them.

15. AWS Panorama

AWS Panorama is a service that brings computer vision (CV) capabilities to on-premises cameras using edge devices. It allows businesses to deploy machine learning models directly on supported hardware (like AWS Panorama Appliance) so they can analyze video feeds locally instead of sending everything to the cloud. This reduces latency and enables real-time decision-making for video-based applications. AWS Panorama integrates with existing IP cameras and uses AI models to detect objects, people, safety violations, and operational events. It is commonly used in industrial monitoring, retail analytics, manufacturing safety, and security systems.

Example:
A retail store can use AWS Panorama to analyze CCTV footage in real time and detect when shelves are running low on products or when customers enter restricted areas.

16. Amazon Monitron

Amazon Monitron is a fully managed industrial monitoring service that uses machine learning to detect potential failures in equipment. It works by using wireless sensors placed on machines like motors, pumps, and compressors to collect vibration and temperature data. This data is sent to AWS, where machine learning models analyze it to identify abnormal patterns that may indicate equipment failure. Amazon Monitron helps businesses implement predictive maintenance, reducing downtime and maintenance costs. AWS provides both the sensors and the cloud service, making it easy to deploy without complex setup.

Example:
A manufacturing company can use Amazon Monitron to monitor factory machines and receive alerts when a motor shows early signs of failure so it can be repaired before breaking down.

17. AWS HealthLake

Amazon HealthLake is a fully managed service that helps healthcare organizations store, transform, query, and analyze health data at scale. It is designed specifically for medical data such as electronic health records (EHRs), clinical notes, lab results, and patient histories. Amazon HealthLake uses machine learning to structure unstructured medical data and convert it into a standardized format (FHIR – Fast Healthcare Interoperability Resources). This makes it easier for doctors, researchers, and healthcare providers to gain insights and improve patient care. AWS handles security, compliance, and infrastructure to meet healthcare data regulations.

Example:
A hospital can use Amazon HealthLake to analyze patient records and identify trends in diseases, helping doctors make better treatment decisions based on historical medical data.

18. Amazon Lookout for equipment

Amazon Lookout for Equipment is a machine learning service that helps businesses detect abnormal behavior in industrial equipment by analyzing sensor data. It monitors data such as pressure, temperature, vibration, and flow from machines and automatically identifies signs of potential failure. The service is designed for predictive maintenance, helping companies fix problems before equipment breaks down. Amazon Lookout for Equipment does not require users to build ML models; it learns normal operating patterns and flags anomalies automatically. It is commonly used in manufacturing, energy, utilities, and industrial operations.

Example:
A power plant can use Amazon Lookout for Equipment to monitor turbines and detect unusual vibration patterns that may indicate an upcoming mechanical failure.

19. Amazon Q Business

Amazon Q Business is a generative AI-powered assistant designed to help employees access and use information across their organization. It connects to company data sources like documents, emails, wikis, databases, and enterprise applications, allowing users to ask questions in natural language and get accurate, context-aware answers. Amazon Q Business can also summarize documents, generate reports, and assist with tasks like drafting emails or creating insights from internal data. It is built with enterprise security controls, ensuring data privacy and access permissions are respected. The service is commonly used to improve productivity, reduce information search time, and support decision-making in organizations.

Example:
An employee in a large company can use Amazon Q Business to ask, “What is our travel reimbursement policy?” and instantly get a summarized answer from internal company documents.

20. AWS HealthOmics

AWS HealthOmics is a fully managed service that helps researchers and healthcare organizations store, analyze, and process large-scale biological and genomic data. It is designed for omics data such as DNA sequencing, RNA analysis, and other molecular biology datasets. AWS HealthOmics provides scalable workflows to run complex bioinformatics pipelines, enabling faster scientific discoveries and medical research. It supports industry-standard formats and integrates with other AWS services for storage, compute, and analytics. AWS manages infrastructure, security, and scaling, allowing researchers to focus on analysis instead of system management.

Example:
A medical research lab can use AWS HealthOmics to analyze human genome data to identify genetic markers linked to diseases like cancer or diabetes.

21. Amazon Nova Act

Amazon Nova Act is an AI-powered service designed to help build and run autonomous agents that can perform actions across applications, websites, and APIs. It enables developers to create systems where AI models don’t just generate text, but can also take real-world actions like filling forms, navigating workflows, calling APIs, and completing multi-step tasks. Amazon Nova Act is part of AWS’s generative AI ecosystem and is focused on automation of business processes using intelligent agents. It is commonly used to reduce manual work in operations, customer support, and enterprise workflows by letting AI handle repetitive tasks end-to-end.

Example:
A company can use Amazon Nova Act to build an AI agent that automatically processes customer refund requests by checking order details, validating eligibility, and initiating the refund workflow without human intervention.

22. Amazon Bedrock

Amazon Bedrock is a fully managed service that allows developers to build and scale generative AI applications using foundation models (FMs) from leading AI companies. It provides access to multiple large language models (LLMs) and image generation models through a single API, without needing to manage underlying infrastructure. Amazon Bedrock lets users customize models with their own data using techniques like fine-tuning and retrieval-augmented generation (RAG). It is commonly used for chatbots, content generation, search, summarization, and intelligent automation. AWS handles scaling, security, and model hosting.

Example:
A company can use Amazon Bedrock to build a customer support chatbot that answers user queries using its internal knowledge base and generates human-like responses instantly.

23. Amazon Bedrock AgentCore

Amazon Bedrock AgentCore is a service within Amazon Bedrock that helps developers build, deploy, and manage AI agents that can perform multi-step tasks using foundation models. It provides the orchestration layer that allows agents to plan actions, call APIs, use tools, and interact with external systems in a controlled and secure way. AgentCore enables developers to define workflows where AI agents can reason, break down tasks, and execute them end-to-end, instead of just generating text responses. It is commonly used for building intelligent automation systems, virtual assistants, and enterprise AI workflows that require decision-making and tool usage. AWS manages scaling, security, and integration with other services.

Example:
A company can use Amazon Bedrock AgentCore to build an AI agent that reads customer emails, understands the issue, checks internal systems, and automatically creates a support ticket or resolves the request.

24. Amazon Q

Amazon Q is a generative AI assistant from AWS designed to help users with coding, cloud management, and business tasks using natural language. It can answer questions about AWS services, generate and debug code, explain architectures, and assist with building and operating cloud applications. Amazon Q is available in different forms such as Amazon Q Developer (for coding and software development) and Amazon Q Business (for enterprise knowledge and productivity). It connects to AWS services and company data sources to provide context-aware responses and automate workflows. It is commonly used to improve developer productivity and simplify cloud operations.

Example:
A developer can use Amazon Q to ask, “Why is my Lambda function failing?” and get an explanation along with suggested fixes based on logs and AWS best practices.

25. Amazon Comprehend Medical

Amazon Comprehend Medical is a fully managed machine learning service that extracts medical information from unstructured text such as doctor’s notes, clinical records, discharge summaries, and prescriptions. It uses natural language processing (NLP) to identify entities like medical conditions, medications, dosages, procedures, and anatomy, and then links them to standardized medical ontologies. The service is designed specifically for healthcare and complies with strict data privacy and security requirements. It helps healthcare organizations convert free-text medical documents into structured data for analysis, billing, and clinical research. AWS manages all ML infrastructure and scaling.

Example:
A hospital can use Amazon Comprehend Medical to automatically extract patient diagnoses and prescribed medications from handwritten doctor notes and store them in a structured electronic health record system.

26. Amazon Lex

Amazon Lex is a fully managed service for building conversational interfaces like chatbots and voice assistants. It uses the same deep learning technologies behind Amazon Alexa to understand natural language and respond to user requests. Amazon Lex provides automatic speech recognition (ASR) and natural language understanding (NLU), allowing applications to process both voice and text inputs. It integrates with AWS services like Lambda, Connect, and other backend systems to perform actions based on user queries. It is commonly used in customer support bots, virtual assistants, and automated response systems.

Example:
A bank can use Amazon Lex to build a chatbot that allows customers to check their account balance or recent transactions by simply typing or speaking a request.

27. Amazon Bio Discovery

Amazon Bedrock BioDiscovery is a generative AI–powered service designed to help researchers analyze and understand complex biological and biomedical data. It enables scientists to use foundation models to explore relationships in areas like genomics, proteins, drug discovery, and disease research. Amazon BioDiscovery helps process large-scale scientific datasets, generate hypotheses, and identify patterns that would be difficult to detect using traditional methods. It is commonly used in pharmaceutical research, biotechnology, and life sciences to accelerate drug development and biomedical discoveries. AWS handles the underlying AI infrastructure, scaling, and data processing.

Example:
A pharmaceutical company can use Amazon Bedrock BioDiscovery to analyze protein structures and identify potential drug candidates for treating diseases like cancer or Alzheimer’s faster than traditional lab methods.

28. AWS HealthImaging

AWS HealthImaging is a fully managed service that helps healthcare providers store, analyze, and process medical imaging data at scale in the cloud. It is designed for large imaging formats such as X-rays, CT scans, MRI scans, and ultrasound images. AWS HealthImaging provides fast, low-latency access to medical images and supports standardized formats like DICOM, making it easier for healthcare applications and AI models to work with imaging data. It helps reduce storage costs while enabling advanced analytics and machine learning on medical images. AWS manages security, compliance, and infrastructure, ensuring sensitive healthcare data is protected.

Example:
A hospital can use AWS HealthImaging to store thousands of MRI scans in the cloud and allow doctors to quickly access and analyze patient images from anywhere.