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When did it all start? Well, that was approx 60 years ago! In 1957, Frank Rosenblatt - an electro-mechanical neural network, the Perceptron, which he trained to recognize images (20x20 “pixels”). In 1975, Paul Werbos published a article describing “backpropagation”, an algorithm allowing better and faster training of neural networks. So, if neural networks have been around for so long, whats wrong with them? Why do we talk about them today?
Amazon Robotics was founded in 2003 on the notion that in order to meet consumer demands in eCommerce, a better approach to order fulfillment solutions was necessary. Amazon Robotics empowers a smarter, faster, more consistent customer experience through automation
automates fulfilment center operations using various methods of robotic technology including autonomous mobile robots, sophisticated control software, language perception, power management, computer vision, depth sensing, machine learning, object recognition, and semantic understanding of commands.
Amazon Prime Air is a service that will deliver packages up to five pounds in 30 minutes or less using small drones and relies extensively on visual object recognition.
We have Prime Air development centers in the United States, the United Kingdom, Austria, France and Israel.
Amazon Go is a new kind of store with no checkout required. We created the world’s most advanced shopping technology so you never have to wait in line. With our Just Walk Out Shopping experience, simply use the Amazon Go app to enter the store, take the products you want, and go! No lines, no checkout. (No, seriously.)
No lines, no checkout
Our checkout-free shopping experience is made possible by the same types of technologies used in self-driving cars: computer vision, sensor fusion, and deep learning. Our Just Walk Out Technology automatically detects when products are taken from or returned to the shelves and keeps track of them in a virtual cart. When you’re done shopping, you can just leave the store. Shortly after, we’ll charge your Amazon account and send you a receipt.
We’re excited to have a broad set of customers running ML on AWS today.
[ADD: Conde Nas – using P2 and TF to detect hand bags https://technology.condenast.com/story/handbag-brand-and-color-detection]
The Data platform
Highly-optimized Machine Learning Algorithms Amazon SageMaker installs high-performance, scalable machine learning algorithms optimized for speed, scale, and accuracy, to run on petabytes of training datasets. Based on the type of learning that you are undertaking, you can choose from supervised algorithms, such as linear/logistic regression or classification; as well as unsupervised learning, such as with k-means clustering.
Linear Classification and Regression Factorization Machines K-Means Clustering Principal Components Analysis (PCA) Latent Dirichlet Analysis (Spectral LDA) Neural Topic Modeling Seqence2Sequence
Gradient Boosted Trees (XGBoost)
An Amazon Resource (has an ARN)
Links the model artifacts (weights) to the inference container (predictions code)
A model can link multiple inference containers and associated model artifacts
A model is used to create a ProductionVariant
One or many ProductionVariants constitute an EndpointConfiguration
An Endpoint Configuration is used to create an Endpoint
Each version has its inference container on ECR and model artifacts on S3
Versions of a model can be used to create multiple ProductionVariants with different weights
The ProductionVariants can be used to create an EndpointConfiguration for the versions of the model
The EndpointConfiguration defines the Endpoint
A ProductionVariant is analogous to an Auto-Scaling Group for a specific IM model. The associated VariantWeight determines the portion of traffic handled by the ProductionVariant.
The Endpoint serves the inference traffic with one or multiple auto-scaling groups (production variants) of models versions. Prod gets 50% of the traffic because its weight contributes 50% of the sum of all VariantWeights in the EndpointConfiguration
Cells are hierarchical decomposition of the sphere Compact, represented by 64 bit int Similar levels represent similar sizes of area Containment query for arbitrary regions are really fast
Projects points/regions of the sphere into a cube and takes each cube face as a quad-tree where the sphere point is projected into it. After that space is discretized and cells are enumerated on a Hilbert curve. Hilbert curve is a space-filling curve that converts multiple dimensions into one dimension while preserving locality
Invoke takes two invocation types: Event, RequestResponse, DryRun (to just verify that you have permissions
We can put this in a cloudformation custom resource