AWS Machine Learning
AWS Machine Learning tools provide several high-level algorithms that provide business intelligence across various data sources, including text, images, and video. We at AllCode have managed to keep pace with the rapid growth machine learning has displayed and will continue to provide technological insights in the future.
Machine learning will continue to be a pivotal technology. At the time of writing, AWS has 34 services that use machine learning and plans to increase that number yearly. These services fall under the following categories.
- AWS-Supported ML Frameworks
- AWS Deep Learning Algorithms
- AWS ML Add-on Services
- Powered Hardware
- Specialized AWS ML Services
AWS-Supported ML 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:
- Amazon SageMaker – Amazon’s in-house machine learning framework.
- PyTorch on AWS – a machine learning framework designed and managed by Facebook’s AI Research (FAIR) Lab.
- Apache MXNet on AWS – Apache Software Foundation’s machine learning framework.
- TensorFlow on AWS – a machine learning framework managed by the Google Brain team.
AWS Deep Learning Algorithms
The most robust offering is the AWS deep learning algorithm. This type spans a large cross-section of data and brings tremendous value without any training. These deep learning algorithms include:
- Amazon Comprehend – discover insights and relationships in text
- Amazon Comprehend Medical – a medical-specific spinoff of Comprehend
- Amazon DevOps Guru – ML-powered cloud operations service to improve application availability
- Amazon Forecast – increase forecast accuracy using machine learning
- Amazon Rekognition – machine learning computer vision to analyze image and video
- Amazon Personalize – create real-time personalized user experiences faster at scale
- Amazon CodeGuru – automate code reviews and optimize application performance with ML-powered recommendations
- Amazon Fraud Detector – a real-time fraud detection service
- Amazon Kendra – an intelligent search service powered by machine learning
- Amazon Textract – extract printed text, handwriting, and data from any document
- Amazon Translate – translate written text from one language to another
- Amazon Transcribe – convert spoken language into written text
- Amazon Lookout for Equipment – detect abnormal behavior by analyzing sensor data
- Amazon Lookout for Metrics – detect anomalies in metrics
- Amazon Lookout for Vision – spot product defects using computer vision to automate quality inspection
AWS Machine Learning Add-on Services
The machine learning add-ons category includes offerings that generally make some of the struggles in machine learning less painful or improve performance. These services include:
- Amazon Augmented AI – implements a more human review of ML predictions
- Amazon Elastic Inference – decreases machine learning inference costs by upwards of 75%
- Amazon SageMaker Ground Truth – builds datasets for training machine learning models
- Amazon SageMaker Neo – runs machine learning models anywhere with up to 25x better performance
- AWS Deep Learning AMIs – Amazon Machine Images (AMI) for different ML frameworks
- AWS Deep Learning Containers – read-to-go containers for different ML frameworks
- Amazon HealthLake – Securely store, transform, query, and analyze health data in minutes
Powered Hardware Options
Like the add-ons, these are designed to augment services and equipment, making operations more efficient or reducing potential costs.
- Amazon Inferentia – high-performance machine learning inference chip, custom-designed by AWS
- AWS DeepLens – a deep learning-enabled video camera
- Amazon Monitron – an end-to-end system for equipment monitoring with an option for a physical sensor.
- AWS Panorama – hardware-enabled computer vision at the edge
Special AWS Machine Learning Services
Some AWS machine learning algorithms are purely designed for particular functions.
- Amazon Lex – build voice and text chatbots
- Amazon Polly – turn text into life-like speech
- AWS DeepComposer – a machine learning-enabled musical keyboard designed to help learn ML concepts
- AWS DeepRacer – autonomous 1/18th scale race car driven by ML
Why Pursue Machine Learning?
While users can build algorithms from scratch using open-source deep learning frameworks like MXNet, Keras, and TensorFlow, most problems or opportunities data presents are not unique. The pre-arranged datasets are problems other organizations have experienced and solved with the data they gathered. Machine learning can be applied to many common situations, such as determining customer purchasing habits, profanity filters, and interpreting trends in customer reviews. Building the necessary solution can require extensive trial and error.
What AWS offers are pre-built, high-level algorithms (in these cases, Personalize, Rekognition, and Comprehend) that are appropriately trained or can be easily trained to understand and handle whatever scenario they are applied to. What would typically take hours of work-time and eat into the company budget can be implemented and trained at a fraction of the cost and time.
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