Amazon (AWS) Machine Learning / AI Services List

January 30, 2024 by Ajitesh Kumar · 2 Comments
amazon machine learning services

Last updated: 30th Jan, 2024

Amazon Web Services (AWS) is a cloud computing platform that offers machine learning as one of its many services. AWS has been around for over 10 years and has helped data scientists leverage the Amazon AWS cloud to train machine learning models. AWS provides an easy-to-use interface that helps data scientists build, test, and deploy their machine learning models with ease. AWS also provides access to pre-trained machine learning models so you can start building your model without having to spend time training it first! You can get greater details on AWS machine learning services, data science use cases, and other aspects in this book – Data Science on AWS.

Different AWS cloud services for AI / Machine learning

The following is a list of AWS cloud services for AI / machine learning. As data scientists, it is of utmost importance to learn about Amazon’s machine learning services to leverage AWS services to the fullest to innovate with AI-based solutions.

General AI and ML Services

  1. Amazon Augmented AI: Easily implement a human review of machine learning predictions. An example use case is streamlining content moderation workflows by integrating human reviews for ambiguous or sensitive content detected by AI.
  2. Amazon Bedrock: Build and scale generative AI applications with foundation models. An example use case is developing AI-driven content creation tools, such as automated article writing or image generation. This can be useful in creating generative AI applications.
  3. Amazon CodeGuru: Intelligent recommendations for building and running modern applications. An example use case is automatically reviewing code to identify critical issues and optimize performance for software development projects. 
  4. AWS DeepComposer: Get started with Generative AI for developers of all skill levels. An example use case is enabling developers and musicians to experiment with AI-generated music compositions through an interactive learning experience.
  5. Amazon DevOps Guru: ML-powered cloud operations service to improve application availability. An example use case is automatically detecting operational issues and providing specific recommendations to reduce mean time to resolve (MTTR) for cloud applications.
  6. AWS DeepRacer: Fully autonomous 1/18th scale race car, driven by machine learning. An example use case is educating developers on reinforcement learning through competitive racing in a global league.
  7. Amazon Forecast: Fully-managed service for accurate time-series forecasting. This service is based on the technology that powers Amazon.com’s demand forecasting needs, such as efficient inventory management, immediate product fulfillment, and same-day delivery. An example use case is enhancing inventory planning and demand forecasting for retail businesses through predictive analytics.
  8. Amazon Fraud Detector: It is a fully managed service that identifies potentially fraudulent online activities such as online payments and fake accounts. Using the Fraud detector service, you can create a fraud-detection model with just a few clicks, a relatively small amount of historical data, and minimal code. An example use case is preventing fraudulent transactions in real time for e-commerce platforms by analyzing transaction patterns.
  9. Amazon Kendra: Highly accurate enterprise search service powered by machine learning. An example use case is improving employee productivity by providing a more intuitive and accurate search across internal documents and knowledge bases.
  10. AWS Panorama: Enabling computer vision applications at the edge. An example use case is optimizing retail store operations by analyzing customer traffic patterns and inventory levels through video analytics.
  11. Amazon Personalize: Easily add real-time recommendations to your apps. An example use case is personalizing user experience on e-commerce sites by delivering tailored product recommendations. Using Amazon Personalize, one can build custom personalization models without having to deal with the complexity of managing our machine learning infrastructure. This service can be used to create individualized product recommendations as well as targeted marketing promotions.
  12. Amazon Q: Generative AI-powered enterprise assistant. An example use case is streamlining business operations by automating customer service responses and internal data queries.
  13. Amazon SageMaker: Build, Train, and Deploy Machine Learning Models. An example use case is accelerating the development of scalable machine learning models for predictive analytics in various industries like finance, healthcare, and more.
  14. AWS AI Kits: Pre-trained AI solutions for common business problems. An example use case is quickly integrating AI capabilities, such as language translation or chatbots, into business applications without extensive machine learning expertise.
  15. AWS DeepLens: Deep Learning Enabled Video Camera. An example use case is facilitating hands-on learning and experimentation with computer vision projects for developers, from facial recognition to object detection.
  16. Amazon Macie: A fully managed security service that uses machine learning to identify sensitive data like personally identifiable information in AWS-based data sources, such as S3. Macie provides visibility into where this data is stored and who is accessing the data. By monitoring access to sensitive data, Macie can send an alert when it detects a leak or the risk of a leak.

Text and Image Analysis

  1. Amazon Comprehend: Analyze Unstructured Text. An example use case is extracting key phrases, entities, and sentiments from customer feedback to enhance product insights and improve customer service.
  2. Amazon Rekognition: Amazon Rekognition helps identify objects, including people, text, and activities found in both images and videos. It uses AutoML to train custom models to recognize objects specific to our use case and business domain. An example use case is implementing facial recognition for user authentication in security systems or personalized customer experiences in retail. Another use case can be detecting violent content in videos uploaded by the application users.
  3. Amazon Textract: Automatically Extract Text and Data. An example use case is digitizing and processing various types of documents such as forms, receipts, and invoices, enabling automated data entry and reducing manual processing in sectors like banking, insurance, and logistics.
  4. Amazon Lex: Build Voice and Text Chatbots. An example use case is developing conversational interfaces for customer service, enabling businesses to automate interactions, handle inquiries, and provide 24/7 support through chatbots on websites or messaging platforms.

Health and Biomedical Services

  1. AWS HealthOmics: Transform omics data into insights. An example use case is enabling researchers and healthcare professionals to analyze large sets of genomic, proteomic, or other omics data to discover new biomarkers or therapeutic targets for personalized medicine.
  2. AWS HealthLake: Making sense of health data. An example use case is aggregating, structuring, and analyzing disparate health data from electronic health records (EHRs), lab systems, and wearable devices to improve patient care and health outcomes.
  3. Amazon Comprehend Medical: Extract insights and relationships from medical text using machine learning. An example use case is processing patient records and clinical notes to identify conditions, medications, and treatments, facilitating faster and more accurate patient care decisions.
  4. AWS HealthImaging: Store, analyze, and share medical images. An example use case is enabling radiologists to more efficiently review and collaborate on patient imaging studies, leading to faster and more accurate diagnoses.

Speech Services

  1. Amazon Polly: Turn Text into Lifelike Speech. An example use case is creating interactive voice responses and audio content for applications, making digital content more accessible to visually impaired users, or providing auditory learning materials.
  2. Amazon Transcribe: Automatic Speech Recognition. An example use case is transcribing customer service calls in real time to enhance quality monitoring, extract insights, and improve customer experience by analyzing the content of the conversations.

Monitoring and Anomaly Detection

  1. Amazon Lookout for Equipment: Detect abnormal equipment behavior by analyzing sensor data. An example use case is predictive maintenance in manufacturing, where the service can identify potential equipment failures before they occur, reducing downtime and maintenance costs.
  2. Amazon Lookout for Metrics: Accurately detect anomalies in your business metrics and quickly understand why. An example use case is monitoring web application traffic for sudden spikes or drops, enabling rapid response to potential issues like website outages or unexpected surges in user engagement.
  3. Amazon Monitron: End-to-end system for equipment monitoring. An example use case is continuous monitoring of critical machinery in industrial settings, allowing for timely detection of issues that could lead to equipment failure, thereby ensuring operational continuity and safety.
Ajitesh Kumar
Follow me
Latest posts by Ajitesh Kumar (see all)

Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. For latest updates and blogs, follow us on Twitter. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking. Check out my other blog, Revive-n-Thrive.com
Posted in AWS, Cloud, Data Science, Machine Learning. Tagged with , , .

Leave a Reply

Your email address will not be published. Required fields are marked *