Artificial intelligence and machine learning are no longer the stuff of science fiction. Organizations of all sizes are using these tools to create innovative artificial intelligence applications – namely, Amazon.com's own retail experience. Join us for an inside look at how Amazon thinks about this technology, and gain insight into a range of new machine learning services on AWS for use in your own organization.
Alex Coqueiro, Solutions Architect, Amazon Web Services
Manu Sud, Manager, Analytics and Advanced Technology Branch, Ontario Ministry of Economic Development, Job Creation and Trade
There isn't a customer today almost that doesn't have a significant data set and that is not making use of machine learning. Many of our AWS customers, sharing just a few here today, use it today every day as part of their core operations.
Formula One Group (Formula 1) is moving the vast majority of its infrastructure from on-premises data centers to AWS, and standardizing on AWS’s machine-learning and data-analytics services to accelerate its cloud transformation. The Formula 1 is responsible for the promotion of the FIA Formula One World Championship, a series of auto racing events in 21 countries where professional drivers race single-seat cars on custom tracks or through city courses in pursuit of the World Championship title. Using Amazon SageMaker, Formula 1’s data scientists are training deep-learning models with 65 years of historical race data to extract critical race performance statistics, make race predictions, and give fans insight into the split-second decisions and strategies adopted by teams and drivers. Formula 1 is also working with AWS to enhance its race strategies, data tracking systems, and digital broadcasts through a wide variety of AWS services.
So we made our ML mission at AWS.... “to “put machine learning in the hands of every developer and data scientist”.
https://www.youtube.com/watch?v=soG1B4jMl2s
Therefore, the only way to capture and index this information is to transcribe it to text.
Current solution is manual, slow and expensive
You want automated, fast and cost effective
Amazon Comprehend is a natural language processing service that uses machine learning to find insights and relationships in text. Amazon Comprehend identifies the language of the text; extracts key phrases, places, people, brands, or events; understands how positive or negative the text is; and automatically organizes a collection of text files by topic.
Our customers are using Amazon Comprehend to identify key topics, entities, and sentiments in social media and news streams, and to enhance their ability to access and aggregate unstructured data from the vast document libraries that exist within their organizations.
Hotels.com has thousands of customer views and comments that are submitted by people who stay at the properties. It’s historically been difficult to find what matters in all this data. By using Amazon Comprehend, Hotels.com is able to uncover the unique characteristics that people like or don’t like about each hotel. Consequently, the company is better able to make recommendations to their users.
Amazon Comprehend has the unique ability to look at millions of documents (not just a single document at a time) in order to identify the topics within these documents. This is called “topic modeling”. It’s extremely useful for grouping large amounts of data for a specific purpose. For instance, a publisher who has thousands of articles might want to show just the business articles to the customers who are interested in business. Or a healthcare provider might want to group all documents based on diagnosis or symptoms.
Amazon Comprehend creates topic models efficiently and cost-effectively as it can, for example, build a model from 300 documents, each about a megabyte in size, in just 45 minutes for $1.80.
Vision is one of the most important senses in the human body, and our ability to think has a lot to do with our ability to see.
Vision is also extremely important to artificial intelligence. Fei-Fei Li, one of the leading computer vision researchers from Stanford University once said, “If we want machines to think, we need to teach them to see”.
Missing child demo
Introducing Amazon Rekognition - a fully managed deep learning based image recognition service. Rekognition was designed from the get-go to run at scale. It comprehends scenes, objects, concepts and faces. Given an image, it will return a list of labels. Given an image with one or more faces,it will return bounding boxes for each face, along with face attributes. Given two images with faces, it will compare the largest face from the source image and find similarity with faces found in the tagret image. Rekognition provides quality face recognition at scale, and supports creation of collection of millions of faces and search of similar faces in the collection.
Now lets dive into each of these features and look at the API that support these features.