20 Amazon (AWS) Machine Learning Services to Know

amazon machine learning services

Amazon Web Services 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 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!

What are different AWS cloud services for machine learning?

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

  1. AWS SageMaker allows you to build, train, deploy and manage your ML models. It also provides automated model tuning. You can use it for text analysis, time series forecasting, or image classification. AWS SageMaker also integrates with AWS data sources such as Amazon RDS, AWS Glue, and RedShift. It provides managed notebooks that allow you to build, train, deploy and manage your ML models. It allows data scientists to seamlessly interact with their data using popular AWS data services.
  2. Amazon SageMaker Neo is an AWS machine learning platform that allows data scientists to easily build, train and deploy models. AWS SageMaker Neo automatically scales your machine learning models, which enables data scientists to focus on creating and tuning algorithms. 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!
  3. Amazon SageMaker RL is an AWS machine learning service that allows data scientists to easily build reinforcement learning (RL) models for robotics systems at the AWS cloud or AWS RoboMaker Edge devices such as AWS Greengrass Core MCUs. This service allows data scientists to train AWS RoboMaker RL agents on AWS SageMaker RL models which allows them to perform reinforcement learning tasks such as exploring, seeking rewards, and avoiding obstacles.
  4. Amazon Textract is a document and data capture service that allows you to create applications for converting paper documents, forms, faxes, and scanned images into digital data. It can recognize over 100 different types of data, including text, numbers, and barcodes. AWS Textract supports the latest OCR technology so you can digitize your documents without compromising accuracy. It also allows users to train their own machine learning models on AWS SageMaker Neo. The advantages of AWS textract include the ability to digitize a wide range of structured and unstructured data, high accuracy in extracting text from documents, and automated transcription. AWS Textract allows you to extract information from files that have incorrect formatting or incomplete content.
  5. Amazon forecast is an AWS machine learning service that allows data scientists to forecast time series data using deep neural networks. It is an easy way to create custom forecasts tailored to your own business or use cases.
  6. Amazon Polly is a deep learning service that turns text into lifelike speech. Amazon Polly uses machine learning models for converting text into high-quality synthesized speech that sounds like a human voice.
  7. AWS DeepLens is a wireless video camera that makes it easy to build deep learning models for the edge without having to write any code or manage servers. You can use this device to develop vision-based machine learning applications at the edge. AWS DeepLens is pre-configured with AWS Greengrass Core, AWS Lambda, and Amazon SageMaker for easy development of machine learning models at the edge.
  8. Amazon Transcribe is an automatic speech recognition (ASR) service that can be used to create transcripts of audio files in minutes without having to spend time on manual transcription work. It uses advanced neural network models based on AWS machine learning services to develop accurate transcripts in multiple languages. AWS Transcribe uses deep neural networks that are trained with automatic speech recognition (ASR) data collected from a large number of speakers and transcribed by humans into text.
  9. Amazon Translate is an automatic translation service that can be used to translate text from one language into another. It uses machine learning models based on AWS cloud services like Amazon SageMaker and AWS DeepLens for accurate translations in multiple languages. Using AWS, you can easily add support for new languages by uploading language models without having to spend time on manual translation work. AWS automatically keeps your machine translation model up-to-date with the latest improvements in deep learning technology.
  10. Amazon Comprehend is an AWS service for analyzing the human language used in textual content such as social media posts, articles, and emails. AWS Comprehend uses machine learning to identify entities such as people, places or events mentioned in text data. It also detects the sentiment of language used (positive/negative) so you can understand what your customers are feeling about your company!
  11. Amazon Lex is a service for building conversational interfaces into any application using voice or text. AWS Lex provides automatic speech recognition (ASR) and natural language understanding (NLU) services to help build chatbots that allow your users to interact with applications by using their voice!
  12. Tensorflow on AWS is a managed service that allows data scientists and machine learning engineers to use the popular Tensorflow framework on AWS. AWS can run your TensorFlow models with high-performance GPUs in Amazon ECX, Amazon Elastic Compute Cloud (Amazon EC), or Amazon Lambda. AWS also provides a pre-built Docker container that you can easily import into your existing workflows without having to build a TensorFlow environment.
  13. PyTorch on AWS is a managed service that allows data scientists and machine learning engineers to use the popular PyTorch framework on AWS. AWS runs your existing Python or Jupyter notebooks using high-performance GPUs in Amazon ECX, AWS Elastic Compute Cloud (Amazon EC), or AWS Lambda.
  14. AWS RoboMaker is an AWS service that allows you to run intelligent robotics applications at the AWS cloud. AWS RoboMaker makes it easy to develop, simulate and deploy intelligent robotics applications at scale in AWS. It provides deep learning pre-trained models for common robotic use cases such as object recognition, depth perception, localization/mapping, navigation, and grasping so you can build your own robots easily.
  15. AWS DeepRacer is a miniature car that allows data scientists to develop machine learning models at the edge using reinforcement learning. AWS DeepRacer car is a great way for data scientists to learn about reinforcement learning (RL) with AWS RoboMaker Edge devices such as AWS Greengrass Core MCUs or test their models before deploying them on real-world robotics systems. It uses AWS RoboMaker so it can be deployed automatically on AWS DeepRacer car. AWS DeepRacer provides a web-based racing simulator and supports reinforcement learning (RL) that allows you to train machine learning models at the AWS cloud, AWS RoboMaker Edge devices such as AWS DeepRacer cars, and AWS Greengrass Core MCUs.
  16. Amazon SageMaker Ground Truth is an easy way for data scientists to label data for machine learning training. AWS SageMaker Ground Truth allows you to label your data with ground truth labels using human workers on Amazon Mechanical Turk or by using the AWS RoboMaker Simulation service. It uses deep neural networks that are trained with automatic speech recognition (ASR) and natural language understanding (NLU) annotations collected from a large number of AWS DeepRacer cars and AWS RoboMaker Edge devices such as AWS Greengrass Core MCUs.
  17. Amazon Rekognition helps identify objects such as people, text, activities, and scenes in images and videos at the AWS cloud. AWS Rekognition allows you to build image search, content moderation, and object detection applications for your own business or use cases such as law enforcement.
  18. Amazon Elastic Inference is a compute service that provides high-performance inference acceleration for machine learning models on AWS. AWS DeepRacer car uses Amazon Elastic Inference to accelerate inference on AWS RoboMaker Edge devices such as AWS Greengrass Core MCUs. It is a fully managed service that supports the TensorFlow framework and allows you to optimize models for use cases like video processing, image search, or text understanding using hardware acceleration of distributed neural networks.
  19. Amazon Augmented AI provides a set of tools and APIs to build augmented intelligence applications that can learn from data. AWS Augmented AI provides automatic speech recognition (ASR), natural language understanding (NLU) and visual search capabilities so you can add voice, text, image or video understanding to your application using AWS Lambda functions at the AWS cloud.
  20. Amazon fraud detector is a machine learning service that helps you detect fraud using AWS. AWS Fraud Detector allows you to monitor your AWS bill and flag suspicious activity automatically so you can take immediate action. AWS Fraud Detector also helps AWS customers protect AWS resources from fraudulent activity and abuse.
Ajitesh Kumar
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Ajitesh Kumar

I have been recently working in the area of Data Science and Machine Learning / Deep Learning. In addition, I am also passionate about various 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. I would love to connect with you on Linkedin.
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