Best 10 AWS ML tools

16 May 2022 | 14 min read
Best 10 AWS ML Tools

Disney, Pinterest, Philips, Volkswagen Group, McDonald’s, Autodesk. What do these companies have in common? International recognition, annual billion-dollar revenues, and the use of AWS machine learning (ML) tools that enable the continuous enhancement and personalisation of both digital and physical products.

Let’s dive right into the top 10 AWS ML tools that have revolutionised data processing in international enterprises and discover how ML-based predictions can improve the user experience, optimise sales funnels, increase user safety, and reduce operational overhead.

The key use cases of best AWS ML tools
Use cases of AI/ML; source: AWS

A personalised approach to machine learning

Everybody knows that machine learning is a powerful tool. That being said, even top-notch AI services do not work like a magic wand. Each business problem requires an individual approach, taking into account your tech stack, the skills of your developers, as well as the financial aspect. However, pairing up the right ML tools with some supervision of experienced data scientists or software developers will enable you to create a tailor-made solution adapted to the specific business goals.

1. Amazon Rekognition

Amazon Rekognition enables the addition of pre-trained or made-to-order computer vision APIs to your apps. This ML tool is capable of scanning millions of images and videos within seconds and extracting insights from the analysed repository. Last but not least, Rekognition is a heavily customisable service – hence it is no exaggeration to say it allows you to solve virtually all problems related to image analysis.

Top features:

  • content moderation: detecting inappropriate or unwanted images, ads, and videos
  • face detection and analysis: detecting and analysing facial attributes (e.g. open eyes, smile, facial hair)
  • labels: recognising objects, scenes, and activities (such as “playing piano” or “studying”)
  • custom labels: detecting brand logos or other objects specific to one’s business needs
  • text detection: extracting skewed or blurry text from images or videos.

Use cases:

  • moderating user-generated content (UGC) in social media
  • online user identity verification
  • improving home automation services by delivering apt, timely alerts 
  • classifying machine parts in an assembly line
  • detecting car plate numbers from traffic cameras.

2. Amazon Personalize

Amazon Personalize is a fully automated recommendation engine that enables the implementation of real-time personalised recommendations based on user activity, item and user similarity. The tool uses advanced ML models and event tracking to deliver a completely customised experience, meet changing user needs, and gradually improve engagement and conversion rates.

Top features:

  • high quality, real-time recommendations: creating in-depth suggestions that respond to the specific needs of the users, as well as developing recommendations for new users (with no historical data)
  • easy integration across channels and devices: providing a unique experience throughout the user journey
  • data protection and privacy: collected data is encrypted and only used to create tailored recommendations 
  • reduced development time: implementing a customised, ML-powered recommendation system in days, not months.

Use cases:

  • personalised content recommendation (e.g. based on user activity) in a new social app
  • increasing content consumption by delivering personalised recommendations of e-books, music, and videos
  • improving marketing communication by personalised push notifications or marketing emails.

3. Amazon Comprehend

Amazon Comprehend is a Natural Language Processing (NLP) service that uses machine learning to extract insights from unstructured textual data. This AWS tool applies sentiment analysis, part-of-speech extraction, and tokenisation to detect critical text features, which can be helpful, e.g. in classifying customer satisfaction surveys.

Top features:

  • simplified document processing workflow: extracting text, key phrases, topics, and more from contracts or forms
  • data protection and privacy: identifying and securing Personally Identifiable Information (PII) from documents
  • qualitative research: uncovering user insights from a text in product reviews, emails, or help desk tickets.

Use cases:

  • automated categorisation of support requests by detecting customer sentiment
  • advanced product reviews indexing by key phrases, sentiment, and context
  • financial documents management, e.g. extracting and classifying entities from sheets and statements.

4. Amazon Lex

Amazon Lex enables building conversational interfaces for apps supporting both text and voice. This tool understands intent, maintains context, and automates simple tasks across many languages.

Amazon Lex AWS tool - how it works?
Amazon Lex – how it works; source: AWS

Top features:

  • intent-aware AI: automating simple tasks across different languages while understanding intent and maintaining context
  • reduced design and development time: designing chatbots based on already existing transcripts
  • effortless connection with other AWS services.

Use cases:

  • implementing a voice chatbot for an e-commerce web service that can answer operational user queries
  • enabling self-service capabilities in a healthcare app, e.g. booking a doctor appointment without reaching a human agent
  • automating FAQ responses.

5. Amazon Polly

Polly is a cloud service that uses deep learning algorithms to convert text to lifelike speech. It currently supports male and female voices across 31 languages (including Japanese, Chinese, Korean, and Arabic) and handles time, dates, units, fractions, and abbreviations. Recently, AWS launched the Brand Voice feature, which enables building an exclusive NTTS voice with the help of the Amazon Polly dev team.

Top features:

  • natural, human-like text-to-speech voices: fluid pronunciation, wide selection of male and female voices, dozens of languages available
  • creating speech files: Polly allows for the replaying and storing of generated speech
  • real-time streaming: fast response time, allowing apps/users to play the voices immediately.

Use cases:

  • creating video tutorial content with no real people required
  • developing a highly personalised, artificial voice conversation in multiple languages (using Amazon Polly, Amazon Lex, Amazon Transcribe and Amazon Translate)
  • karaoke-style text highlighting in an e-learning app.

6. Amazon Transcribe

Amazon Transcribe provides an API to convert speech to text. This AWS tool lets you obtain high-quality, intelligent, real-time transcriptions – e.g. tuned to high or low-fidelity audio.

Key features:

  • gathering insights from audio and video files: extracting information from customer calls, clinical conversations, and more
  • enhanced accuracy: developing custom, domain-specific models
  • automatic language identification
  • data protection and privacy: masking sensitive customer information.

Use cases:

  • developing an audio-to-text converter app  
  • customer call analytics
  • converting audio and video assets into text archives
  • creating subtitles to increase accessibility in a mobile app.

7. Amazon Translate

Amazon Translate exploits neural networks and deep learning models to deliver fast, high-quality, and natural-sounding text translations. This AWS tool supports 75 languages and customises the vocabulary by defining specific phrases or uploading brand names.

Key features:

  • continuous improvement: more and more accurate translations based on an expanding dataset
  • instant on demand translations and efficient bulk translations
  • customisation: generating a personalised output complying with unique brand terminology
  • versatility: translating different content formats, including docx, pptx, xlsx and HTML documents.

Use cases:

  • translating real-time content in social media
  • sentiment analysis performed across different languages and countries (using Amazon Translate and Amazon Comprehend)
  • cross-lingual communication between app users.

8. Amazon Textract

Amazon Textract is a ML service that automatically extracts printed text, handwriting, forms and tables from any scanned document. The tool allows human reviews to check PII and encrypts the collected data to meet rigid data privacy standards.

Key features:

  • no manual configuration required: automatic extraction of text and structured data (tables, forms) from printed documents
  • intelligent text recognition: extracting relationships and structure from the analysed data.

Use cases:

  • extracting specific parts of documents from an extensive database
  • extracting business data from financial forms to accelerate loan applications.

9. Amazon Lookout for Vision

Lookout for Vision reduces operational costs by spotting defects or anomalies in live process lines. This ML tool helps to improve product quality and prevent unexpected technical issues.

Key features:

  • automated quality control: spotting damage or anomalies during the entire production process
  • determining missing components
  • continuous improvement: constant verification of model predictions.

Use cases:

  • spotting defects on ceramic tiles production line in real time
  • car damage detection
  • automatic cancer tissue detection – by analysing microscopic images of tissues with visible cancer cells, Lookout can create a model prediction and automatically detect further anomalies.

10. Amazon OpenSearch Service

Amazon OpenSearch Service provides personalised search possibilities, allowing users to browse all available spaces, documents and databases – up to petabytes of unstructured data. OpenSearch reduces operational overhead with automated provisioning while enhancing the performance analysis capabilities simultaneously.

Amazon OpenSearch Service - how it works?
Amazon OpenSearch Service – how it works; source: AWS

Key features:

  • quick, elastic search and analysis of unstructured data (posts, users, logs, databases)
  • security management: analysing logs from different sources across your network.

Use cases:

  • monitoring and debugging apps
  • real-time threat detection

Accelerate innovation in your business with ready-made ML solutions

Machine learning allows companies to enhance their products by providing impeccable customer service, increasing operation speed, and exploring new business areas. AWS tools, in turn, enable the rapid implementation of intelligent, personalised solutions, providing users with everything they need at every stage of their business journey. 

Are you curious about how to design an AWS architecture tailored to your needs? As a certified Amazon Web Services APN Select Consulting Partner, Miquido assists clients in implementing bespoke ML solutions!

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The controller of your personal data is Miquido sp. z ograniczoną odpowiedzialnością sp.k. with its registered office in Krakow, ul. Zabłocie 43a, 30-701 Kraków. We process the above information in order to answer your questions, contact you and conduct business communication, and if you tick the checkbox, to send you messages containing commercial, business and marketing materials.
The basis for the processing of your data is your consent and Miquido's legitimate interest.You can unsubscribe from the marketing communications at any time. You also have the right to access data, the right to request rectification, deletion or limitation of their processing, data transfer, the right to object, as well as the right to lodge a complaint to the supervisory body. Full information about processing of personal data can be found in the Privacy Policy

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