Instructional Theory


Oracle open sources Graphpipe to standardize machine learning model deployment

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Oracle, a company not exactly known for having the best relationship with the open source community, is releasing a new open source tool today called Graphpipe, which is designed to simplify and standardize the deployment of machine learning models. The tool consists of a set of libraries and tools for following the standard. Vish Abrams, whose background includes helping develop OpenStack at NASA and later helping launch Nebula, an OpenStack startup in 2011, is leading the project. He says as his team dug into the machine learning workflow, they found a gap. While teams spend lots of energy developing a machine learning model, it's hard to actually deploy the model for customers to use.


Shrinking Machine Learning Models for Offline Use : Alexa Blogs

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Last week, the Alexa Auto team announced the release of its new Alexa Auto Software Development Kit (SDK), enabling developers to bring Alexa functionality to in-vehicle infotainment systems. The initial release of the SDK assumes that automotive systems will have access to the cloud, where the machine-learning models that power Alexa currently reside. But in the future, we would like Alexa-enabled vehicles -- and other mobile devices -- to have recourse to some core functions even when they're offline. That will mean drastically reducing the size of the underlying machine-learning models, so they can fit in local memory. At the same time, third-party developers have created more than 45,000 Alexa skills, which expand on Alexa's native capabilities, and that number is increasing daily.


Oracle open sources GraphPipe, a new standard for machine learning models

ZDNet

Machine learning is expected to transform industries. However, its adoption in the enterprise has been slower than some might expect because it's difficult for organizations to deploy and manage machine learning technology on their own. Part of the challenge is that machine learning models are often trained and deployed using bespoke techniques, making it difficult to deploy models across servers or within different departments. Here's how it's related to artificial intelligence, how it works and why it matters. Oracle is aiming to solve this challenge with a new open source, high-performance standard network protocol for transmitting tensor data.


How to Reduce Variance in a Final Machine Learning Model

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A final machine learning model is one trained on all available data and is then used to make predictions on new data. A problem with most final models is that they suffer variance in their predictions. This means that each time you fit a model, you get a slightly different set of parameters that in turn will make slightly different predictions. Sometimes more and sometimes less skillful than what you expected. This can be frustrating, especially when you are looking to deploy a model into an operational environment.


Machine-Learning Models Capture Subtle Variations in Facial Expressions

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MIT Media Lab researchers have developed a machine-learning model that takes computers a step closer to interpreting our emotions as naturally as humans do. By using extra training data, the model can also be adapted to an entirely new group of people, with the same efficacy. Personalized machine-learning models capture subtle variations in facial expressions to better gauge how we feel. MIT Media Lab researchers have developed a machine-learning model that takes computers a step closer to interpreting our emotions as naturally as humans do. In the growing field of "affective computing," robots and computers are being developed to analyze facial expressions, interpret our emotions, and respond accordingly.


Machine-Learning Models Capture Subtle Variations in Facial Expressions – Tech Check News

#artificialintelligence

MIT Media Lab researchers have developed a machine-learning model that takes computers a step closer to interpreting our emotions as naturally as humans do. Personalized machine-learning models capture subtle variations in facial expressions to better gauge how we feel. In the growing field of "affective computing," robots and computers are being developed to analyze facial expressions, interpret our emotions, and respond accordingly. Applications include, for instance, monitoring an individual's health and well-being, gauging student interest in classrooms, helping diagnose signs of certain diseases, and developing helpful robot companions.


Building a Custom Machine Learning Model on Android with TensorFlow Lite – Riggaroo - Android Dev

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Building a custom TensorFlow Lite model sounds really scary. As it turns out, you don't need to be a Machine Learning or TensorFlow expert to add Machine Learning capabilities to your Android/iOS App. ML Kit is a set of APIs provided by Firebase that provide Face Detection, Barcode Scanning, Text Recognition, Landmark Detection and Image Labelling. Some of these APIs provide an offline-mode which enables you to use these features without worrying if a user has an internet connection. ML Kit is great for the common use cases described above, but what if you have some very specific use case?


Building a Custom Machine Learning Model on Android with TensorFlow Lite – Riggaroo - Android Dev

#artificialintelligence

Building a custom TensorFlow Lite model sounds really scary. As it turns out, you don't need to be a Machine Learning or TensorFlow expert to add Machine Learning capabilities to your Android/iOS App. One of the simplest ways to add Machine Learning capabilities is to use the new ML Kit from Firebase recently announced at Google I/O 2018. ML Kit is a set of APIs provided by Firebase that provide Face Detection, Barcode Scanning, Text Recognition, Landmark Detection and Image Labelling. Some of these APIs provide an offline-mode which enables you to use these features without worrying if a user has an internet connection.


Deploying Keras Deep Learning Models with Flask – Towards Data Science

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This post demonstrates how to set up an endpoint to serve predictions using a deep learning model built with Keras. It first introduces an example using Flask to set up an endpoint with Python, and then shows some of issues to work around when building a Keras endpoint for predictions with Flask. The goal of this post is to show how to set up a Keras model as an endpoint on an EC2 instance with AWS. Some of the issues that I'll cover include handling a custom metric when using model persistence with Keras, dealing with multi-threading concerns when using Keras in combination with Flask, and getting it all running on an EC2 instance. The complete code listing for this post is available on GitHub.


Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models

arXiv.org Machine Learning

Interpretation and diagnosis of machine learning models have gained renewed interest in recent years with breakthroughs in new approaches. We present Manifold, a framework that utilizes visual analysis techniques to support interpretation, debugging, and comparison of machine learning models in a more transparent and interactive manner. Conventional techniques usually focus on visualizing the internal logic of a specific model type (i.e., deep neural networks), lacking the ability to extend to a more complex scenario where different model types are integrated. To this end, Manifold is designed as a generic framework that does not rely on or access the internal logic of the model and solely observes the input (i.e., instances or features) and the output (i.e., the predicted result and probability distribution). We describe the workflow of Manifold as an iterative process consisting of three major phases that are commonly involved in the model development and diagnosis process: inspection (hypothesis), explanation (reasoning), and refinement (verification). The visual components supporting these tasks include a scatterplot-based visual summary that overviews the models' outcome and a customizable tabular view that reveals feature discrimination. We demonstrate current applications of the framework on the classification and regression tasks and discuss other potential machine learning use scenarios where Manifold can be applied.