Metal Toad - AWS Machine Learning

AWS Machine Learning

AWS Machine Learning tools provide a number of high-level algorithms that provide business intelligence across various data sources including text, images, and video. At Metal Toad we are at the beginning of what will become a machine-learning revolution, where businesses that have embraced machine-learning early will outperform their competition.

If you are brand new to the topic of machine learning, rather than diving into the specific AWS machine learning services you may want to explore the topic of what is deep learning first.

According to the AWS website primary machine learning, there are 32 machine learning services (as of 2022), however, this number is a miscount. By digging a little further you can find a total of 34 AWS machine learning services. Within this broad umbrella of machine learning, there are five major categories:

  • AWS Supported ML Frameworks

  • AWS Deep Learning Algorithms

  • Add-on Services

  • Powered Hardware

  • Special AWS ML Services

AWS Supported Machine Learning Frameworks

Among the AWS machine learning services offered, the machine learning frameworks are the most rudimentary. AWS provides the hardware and optimizes performance for the following write-your-own-algorithm frameworks:

  1. Amazon SageMaker - Amazon's very own machine learning framework.
  2. PyTorch on AWS -  a machine learning framework managed by Facebook's AI Research (FAIR) Lab.
  3. Apache MXNet on AWS - a machine learning framework from the Apache Software Foundation.
  4. TensorFlow on AWS - a machine learning framework managed by the Google Brain team.

In addition to these AWS also does support the following via their available deep-learning Amazon Machine Images (AMIs):

  • Chainer
  • Theano
  • Keras
  • Gluon

AWS Deep Learning Algorithms

The most robust offering —and by far the most interesting— is the AWS deep learning algorithm which spans a large cross-section of data and brings a tremendous amount of value with no need for any kind of training. These deep learning algorithms include:

  1. Amazon Comprehend - discover insights and relationships in text
  2. Amazon Comprehend Medical - a medical-specific spinoff of Comprehend
  3. Amazon DevOps Guru - ML-powered cloud operations service to improve application availability
  4. Amazon Forecast - increase forecast accuracy using machine learning
  5. Amazon Rekognition - machine learning computer vision to analyze image and video
  6. Amazon Personalize - create real-time personalized user experiences faster at scale
  7. Amazon CodeGuru - automate code reviews and optimize application performance with ML-powered recommendations
  8. Amazon Fraud Detector - a real-time fraud detection service
  9. Amazon Kendra - an intelligent search service powered by machine learning
  10. Amazon Textract - extract printed text, handwriting, and data from any document
  11. Amazon Translate - translate written text from one language to another
  12. Amazon Transcribe - convert spoken language into written text
  13. Amazon Lookout for Equipment - detect abnormal behavior by analyzing sensor data
  14. Amazon Lookout for Metrics - detect anomalies in metrics
  15. Amazon Lookout for Vision - spot product defects using computer vision to automate quality inspection

AWS Machine Learning Add-on Services

Not exactly stand-alone products (but branded that way), the machine learning add-ons category includes offerings that generally make some of the drudgery involved in machine learning less painful or somehow improve performance. These services include:

  1. Amazon Augmented AI - easily implement a human review of ML predictions
  2. Amazon Elastic Inference - lower machine learning inference costs by up to 75%
  3. Amazon SageMaker Ground Truth - create datasets for training machine learning models
  4. Amazon SageMaker Neo - run ML models anywhere with up to 25x better performance
  5. AWS Deep Learning AMIs - Amazon Machine Images (AMI) for different ML frameworks
  6. AWS Deep Learning Containers - read-to-go containers for different ML frameworks
  7. Amazon HealthLake - Securely store, transform, query, and analyze health data in minutes

AWS Machine Learning Powered Hardware

  1. Amazon Inferentia - high-performance machine learning inference chip, custom designed by AWS
  2. AWS DeepLens - a deep learning-enabled video camera
  3. Amazon Monitron - an end-to-end system for equipment monitoring (including a physical sensor)
  4. AWS Panorama - hardware-enabled computer vision at the edge

Special AWS Machine Learning Services

Not exactly deep learning algorithms, there are some AWS machine learning algorithms that really are their own strange little thing.  These include:

  1. Amazon Lex - build voice and text chatbots
  2. Amazon Polly - turn text into life-like speech
  3. AWS DeepComposer - a machine learning-enabled musical keyboard designed to help learn ML concepts
  4. AWS DeepRacer - autonomous 1/18th scale race car, driven by ML

"Machine Learning will empower and improve every business, every government organization, every philanthropy - basically there's no institution in the world that cannot be improved with Machine Learning"

Why use AWS Machine Learning Tools?

While it is possible to build your own algorithm using open-source deep learning frameworks like MXNet, Keras, and TensorFlow most problems or opportunities presented by data are not unique. They are problems that other organizations have seen and solved. Some examples of this are:

  • What products are individual customers most likely to purchase as an upsell?
  • For content being posted by users can we use a machine to tell if it is objectionable (nudity, profanity, etc.)?
  • Is there a way we can pull out themes and sentiments from large amounts of customer product reviews?

The build-your-own solution requires massive amounts of trial and error, overseen by teams of data scientists. Thankfully, this work has already been done by AWS and high-level algorithms (in these cases Personalize, Rekognition, and Comprehend) can simply be implemented and trained at a fraction of the cost and time.

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Need clarification?

Still not sure if your company is ready to implement machine learning, we'd be happy to chat in a no-obligation call or you can take a look at our Machine Learning FAQ.

I'm not in the tech sector. Do I need to be thinking about machine learning?

Machine learning is going to affect every industry from legal, to streaming, to retail. Disruption may come in the form of insights to enable more competitive decision-making all the way to massive workforce replacement (knowledge workers are particularly vulnerable).

How many AWS Machine Learning services are there?

AWS Machine learning now includes 35 distinct products/services. This makes it one of the most robust areas within the AWS ecosystem, with more complexity and continued development than both the AWS Compute and Storage services areas combined.


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