Machine learning is a subfield of artificial intelligence, primarily defined as a machine’s capacity to imitate intelligent human behavior. Artificial intelligence procedures can perform complex tasks similar to how humans solve problems. Machine learning is utilized in internet search engines, email filters to sort out spam, websites to make personalized recommendations, banking software to detect unusual transactions, and many apps on our phones, such as voice recognition. This article will look at the many machine learning services offered in Amazon Web Services Cloud.
- AWS Rekognition
- AWS Transcribe
- AWS Polly
- AWS Translate
- AWS Lex
- AWS Connect
- AWS Comprehend
- AWS SageMaker
- AWS Forecast
- AWS Kendra
- AWS Personalize
- AWS Textract
Amazon Rekognition Video can detect objects, scenes, faces, celebrities, text, and inappropriate video content. You can also search for faces appearing in a video using your repository or collection of face images. You can train a model using the Amazon Rekognition Custom Labels console or the Amazon Rekognition Custom Labels API. Amazon Rekognition stores information about recognized faces in server-side containers known as collections. You can use the facial data stored in a group to search for known faces in images, stored videos, and streaming videos. Amazon Rekognition supports the IndexFaces operation. Amazon RDS and Amazon DynamoDB are managed database services in AWS.
• Find objects, people, text, and scenes in images and videos using ML
• Facial analysis and facial search to do user verification, people counting. Create a database of “familiar faces” or compare them against celebrities
• Use cases:
• Content Moderation
• Text Detection
• Face Detection and Analysis (gender, age range, emotions)
• Face Search and Verification
• Celebrity Recognition
• Pathing (ex: for sports game analysis)
Amazon Rekognition can be used by a social media platform in which users upload and share photos with other users to identify and remove inappropriate photos even though the company has no machine learning (ML) scientists and must build this detection capability with no ML expertise.
- An Introduction to AWS Rekognition | by Ram Vegiraju (towardsaws.com)
- Amazon Rekognition: What is it and How does it Work? – Decipher (www.decipherzone.com)
- Amazon Textract VS Amazon Rekognition – compare differences (xrv.dixiesewing.com)
- DeepPy VS Amazon Rekognition – compare differences & reviews? (nike.btarena.com)
- BrainCore VS Amazon Rekognition – compare differences (ehtif.hedbergandson.com)
Amazon Textract can automate the process of a company accepting enrollment applications on handwritten paper forms where they scan the forms and capture the enrollment data from scanned PDF files to enter the form data into its backend systems.
Amazon Transcribe converts audio input into text, which opens the door for various text analytics applications on voice input. For instance, customers can perform sentiment analysis or extract entities and critical phrases by using Amazon Comprehend on the restored text data from Amazon Transcribe. Amazon Transcribe is the number 1 ranked solution in top Speech-To-Text Services. PeerSpot users give Amazon Transcribe an average rating of 7.6 out of 10. Using Amazon Transcribe, customers can now use 31 supported languages for transcription use cases, such as improving customer service, captioning and subtitling, meeting accessibility requirements, and cataloging audio archives.
• Automatically convert speech to text
• Uses a deep learning process called automatic speech recognition (ASR) to convert speech to text quickly and accurately
• Automatically remove Personally Identifiable Information (PII) using Redaction
• Use cases:
• transcribe customer service calls
• automate closed captioning and subtitling
• generate metadata for media assets to create a fully searchable archive
- How to use AWS Transcribe to transcribe video — Shotstack (shotstack.io)
- How to Use AWS Transcribe to Convert Speech to Text – How-To Geek (www.howtogeek.com)
- How to use AWS Transcribe – Automation Rhapsody (automationrhapsody.com)
- Is AWS Transcribe HIPAA compliant? – Paubox (www.paubox.com)
- Generating text from audio using Amazon Transcribe and AWS (medium.com)
Amazon Polly is a service that turns text into lifelike speech, allowing you to create applications that talk and build entirely new categories of speech-enabled products. Polly’s Text-to-Speech (TTS) service uses advanced deep learning technologies to synthesize natural-sounding human speech. Everyday use cases for Amazon Polly include, but are not limited to, mobile applications such as newsreaders, games, eLearning platforms, accessibility applications for visually impaired people, and the rapidly growing segment of IoT. Amazon Polly can be used to turn text into lifelike speech.
• Turn text into lifelike speech using deep learning
• Allowing you to create applications that talk
- What is Amazon Polly? – W3Schools (www.w3schools.com)
- AWS Polly Archives – DrVoIP Cloud Solutions for Call Centers (drvoip.com)
- PowerShell Gallery | AWS.Tools.Polly 4.1.179 (www.powershellgallery.com)
- GitHub – jhndnnqsc/aws-polly: Q-SYS AWS Polly TTS module (github.com)
- aws polly and aws comprehend | Blake Green – GreenGoCloud (greengocloud.com)
Amazon Translate is a text translation service that uses advanced machine learning technologies to provide high-quality translation on demand. You can use Amazon Translate to translate unstructured text documents or to build applications that work in multiple languages. This year, Intento ranked Amazon Translate #1 among top-performing machine translation providers in its The State of Machine Translation 2020 report.
• Natural and accurate language translation
• Amazon Translate allows you to localize content – such as websites and applications – for international users and to translate large volumes of text efficiently and quickly.
- Translate — Boto3 Docs 1.24.82 documentation – Amazon Web (boto3.amazonaws.com)
- Amazon Translate Using .NET MVC & C# – Codemoto (codemoto.io)
- Using an AWS Service – Amazon Translate as an Example (heardlibrary.github.io)
- aws-translate · GitHub Topics · GitHub (github.com)
- Translating with Amazon Translate (AWS) Using Pairaphrase (www.pairaphrase.com)
- 💥 Amazon Translate Review – Features, languages, pricing – EHLION (ehlion.com)
- How to use aws-sdk for NodeJS with AWS Translate – firxworx (firxworx.com)
Amazon Lex is an AWS service for building conversational interfaces for voice and text applications. With Amazon Lex, the same conversational engine that powers Amazon Alexa is now available to any developer, enabling you to build sophisticated, natural language chatbots into your new and existing applications. Amazon Lex is AWS’s natural language processing (NLP) service that powers conversational AI solutions for voice and chat. It is a potent tool that is relatively easy to start, assuming you have developer credentials on AWS. Amazon Lex natively supports integration with AWS Lambda for data retrieval, updates, and business logic execution. The serverless compute capacity allows effortless execution of business logic at scale while you focus on developing bots.
• Amazon Lex: (the same technology that powers Alexa)
• Automatic Speech Recognition (ASR) to convert speech to text
• Natural Language Understanding to recognize the intent of the text and callers • Helps build chatbots, call center bots
- What is AWS Lex? | Amazon Lex | Tutorials Link (tutorialslink.com)
- AWS Makes Amazon Lex Available to all Customers (press.aboutamazon.com)
- Integrate AWS Lex with MS Teams | AWS re:Post (repost.aws)
- aws lambda – Amazon AWS Lex slot type list – Stack Overflow (stackoverflow.com)
- How to Use AWS Lex to Build Interactive Chatbots – How-To Geek (www.howtogeek.com)
- How to Build an Angular Bot With AWS Lex – ByteScout (bytescout.com)
Amazon Connect is an omnichannel cloud contact center. You can set up a contact center in a few steps, add agents located anywhere, and start engaging with your customers. You can create personalized experiences for your customers using omnichannel communications.
• Amazon Connect:
• Receive calls, create contact flows, cloud-based virtual contact center
• Can integrate with other CRM systems or AWS
• No upfront payments, 80% cheaper than traditional contact center solutions
Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in a text. No machine learning experience is required. Amazon Comprehend uses machine learning to help reveal your unformed data’s insights and associations. Amazon Comprehend uses a Latent Dirichlet allocation-based learning model to choose the topics in a group of documents. It examines each record to determine the context and significance of a word.
• For Natural Language Processing – NLP
• Fully managed and serverless service
• Uses machine learning to find insights and relationships in text • Language of the text
• Extracts critical phrases, places, people, brands, or events
• Understands how positive or negative the text is
• Analyzes text using tokenization and parts of speech
• Automatically organizes a collection of text files by topic
• Sample use cases:
• analyze customer interactions (emails) to find what leads to a positive or negative experience
• Create, and groups articles by topics that Comprehend will uncover
If a company wants to perform sentiment analysis on customer service email messages that it receives and wants to identify whether the customer service engagement was positive or negative, it can use Amazon Comprehend.
- What is AWS Comprehend? – Decipher Zone (www.decipherzone.com)
- AWS Comprehend: Illustrated How-to Guide – Data Demystified (zyabkina.com)
- An Introduction to AWS Comprehend | by Ram Vegiraju | Towards AWS (towardsaws.com)
- Comprehend — Boto3 Docs 1.24.84 documentation – Amazon (boto3.amazonaws.com)
- Amazon Comprehend for Named Entity Recognition (walkingtree.tech)
- amazon web services – Aws comprehend custom model (stackoverflow.com)
Amazon SageMaker is a fully-managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker includes modules used together or independently to build, train, and deploy your machine learning models. SageMaker does not allow you to schedule training jobs. SageMaker does not provide a means for quickly following metrics logged during training. We often fit component extraction and model channels. We can inject the model artifacts into AWS-provided containers, but we cannot inject the feature extractors.
• Fully managed service for data scientists to build ML models • Typically, challenging to do all the processes in one place + provision servers • Machine learning process (simplified): predicting your exam score
Amazon Forecast is a fully managed service that uses machine learning to deliver highly accurate forecasts. Based on the same technology used at Amazon.com, Amazon Forecast uses machine learning to combine time series data with additional variables to build forecasts. Demand Forecasting & Planning solutions on AWS provide sophisticated ML and deep learning models that can incorporate detailed internal and external data to improve demand planning and inventory management.
• Fully managed service that uses ML to deliver highly accurate forecasts • Example: predict the future sales of a raincoat
• 50% more accurate than looking at the data itself
• Reduce forecasting time from months to hours
• Use cases: Product Demand Planning, Financial Planning, Resource Planning,
Amazon Kendra is a highly accurate and easy-to-use enterprise search service powered by machine learning (ML). It allows developers to add search capabilities to their applications so their end users can discover information stored within the vast content spread across their company. Kendra is serverless, so application owners don’t have to manage the underlying infrastructure that performs searches. However, they might have to organize data sources, depending on where data is stored.
• Fully managed document search service powered by Machine Learning
• Extract answers from within a document (text, pdf, HTML, PowerPoint, MS Word, FAQs…) • Natural language search capabilities
• Learn from user interactions/feedback to promote preferred results (Incremental Learning) • Ability to manually fine-tune search results (importance of data, freshness, custom)
Amazon Personalize is a fully managed machine learning service that uses your data to generate item recommendations for your users. It can also create user segments based on the user’s affinity for specific items or item metadata. Amazon Personalize uses recipes, which combine the learning algorithm with the hyperparameters and datasets used. Training a model with different recipes leads to different results. The resultant models are what’s known as a solution version.
• Fully managed ML service to build apps with real-time personalized recommendations
• Example: personalized product recommendations/re-ranking, customized direct marketing • Example: User bought gardening tools, provide recommendations on the next one to buy
• Same technology used by Amazon.com
• Integrates into existing websites, applications, SMS, email marketing systems,
• Implement in days, not months (you don’t need to build, train, and deploy ML solutions) • Use cases: retail stores, media, and entertainment…
Amazon Textract is a machine learning (ML) service that automatically extracts text, handwriting, and data from scanned documents. It goes beyond simple optical character recognition (OCR) to identify, understand, and extract data from forms and tables.
• Extract data from forms and tables
• Read and process any type of document (PDFs, images)
• Use cases:
• Financial Services (e.g., invoices, financial reports)
• Healthcare (e.g., medical records, insurance claims)
• Public Sector (e.g., tax forms, ID documents, passports)
AWS Cost Anomaly Detection is an AWS Service that uses machine learning to continuously monitor cost and usage for unusual cloud spending.