Building AI-powered Serverless Applications on AWS

Editor's Notes

• #4: you have a lot to cover and you are happy to field questions after the talk.
• #12: A trillion is 1,000,000,000,000, also know as 10 to the 12th power, or one million million. It’s such a large number it’s hard to get your head around it, so sometimes trillion just means “wow, a lot.”
• #13: AWS is an AI enabler .. For all the reason mentioned here – When AWS was established in 2006, one core premise was to allow anyone, even a student in his door-room, to get access to the same technologies that Fortune 500 companies have – we called it democratization of technology.
• #16: And the result of this is that we see a ton of machine learning up on AWS today, literally from A through to Z. So everything from Ancestry, who are using machine learning and deep learning to be able to process genomic information and build out family trees, all the way through to Zillow, who use machine learning to do house-price estimation up on the website.
• #17: Amazon Web Services provides a rich ecosystem to help you build smarter applications. In this context, it is worth highlighting the higher level AI services based on deep learning algorithms, like Amazon Rekognition, an image recognition service, Amazon Polly, a text to speech synthesizer, and Amazon Lex, a voice and text chatbot service. We also provide the infrastructure including GPU EC2 instances for fast parallel processing which you can use in combination with any of the popular deep learning libraries like Apache mxnet, Tensorflow, Theano, etc, all of which are available on the AWS deep learning AMI. For your general machine learning purposes, you can also use EC2, Amazon Elastic MapReduce and Spark with SparkML to run any machine learning algorithm. Another popular library is the python scikit-learn, which you can deploy on AWS Lambda or containers, or EC2. So what I am trying to convey is that there is a lot of choice, which basically boils down to picking the right tool for the right job, where you can make trade-offs between ‘do your own’ with all the flexibility, or picking a managed solution which allows you to get results fast without having to do the heavy lifting.
• #18: The basics are pretty simple, but the service has deep functionality. You can send the service a simple string of text, and it will generate the life like voice in your choice of 47 different voices. But it’s not naive of the context of the text. For example, the text here - ‘WA’ and ‘degree F’, that would sound strange if it were spoken out loud. Instead, Polly will automatically expand the text strings ‘WA’ and ‘degree F’, to ‘Washington’ and ‘degrees fahrenheit’, to create more life like speech. The developer doesn’t have to do anything - just send the text, and get life like voice back.
• #26: 30
• #29: 24
• #30: a fully managed deep learning based image recognition service. 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.
• #34: Image moderation Rekognition automatically detects explicit or suggestive adult content in your images, and provides confidence scores.
• #39: 26
• #44: 24
• #51: 24