While artificial intelligence (AI) has become widespread, many commercial AI systems are not yet accessible to individual researchers nor the general public due to the deep knowledge of the systems required to use them. We believe that AI has matured to the point where it should be an accessible technology for everyone. We present an ongoing project whose ultimate goal is to deliver an open source, user-friendly AI system that is specialized for machine learning analysis of complex data in the biomedical and health care domains. We discuss how genetic programming can aid in this endeavor, and highlight specific examples where genetic programming has automated machine learning analyses in previous projects.
The purpose of this site is to provide general information about the hot new field of automated machine learning (AutoML) and to provide links to our own PennAI accessible artificial intelligence system and Tree-Based Pipeline Optimization Tool (TPOT) algorithm and software for AutoML using Python and the scikit-learn machine learning library. We also provide links to some other commonly used AutoML methods and software. The goal of AutoML is to make machine learning more accessible by automatically generating a data analysis pipeline that can include data pre-processing, feature selection, and feature engineering methods along with machine learning methods and parameter settings that are optimized for your data. Each of these steps can be time-consuming for the machine learning expert and can be debilitating for the novice. These methods enable data science using machine learning thus making this powerful technology more widely accessible for those hoping to make use of big data.
Abstract--While the Machine Learning (ML) landscape is evolving rapidly, there has been a relative lag in the development of the "learning systems" needed to enable broad adoption. Furthermore, few such systems are designed to support the specialized requirements of scientific ML. Here we present the Data and Learning Hub for science (DLHub), a multi-tenant system that provides both model repository and serving capabilities witha focus on science applications. First, its selfservice modelrepository allows users to share, publish, verify, reproduce, and reuse models, and addresses concerns related to model reproducibility by packaging and distributing models and all constituent components. Second, it implements scalable and low-latency serving capabilities that can leverage parallel and distributed computing resources to democratize access to published modelsthrough a simple web interface. Unlike other model serving frameworks, DLHub can store and serve any Python 3-compatible model or processing function, plus multiple-function pipelines. We show that relative to other model serving systems including TensorFlow Serving, SageMaker, and Clipper, DLHub provides greater capabilities, comparable performance without memoization and batching, and significantly better performance when the latter two techniques can be employed. We also describe early uses of DLHub for scientific applications. I. INTRODUCTION Machine Learning (ML) is disrupting nearly every aspect of computing. Researchers now turn to ML methods to uncover patterns in vast data collections and to make decisions with little or no human input. As ML becomes increasingly pervasive, newsystems are required to support the development, adoption, and application of ML. We refer to the broad class of systems designed to support ML as "learning systems." Learning systems need to support the entire ML lifecycle (see Figure 1), including model development [1, 2]; scalable training across potentially tens of thousands of cores and GPUs ; model publication and sharing ; and low latency and highthroughput inference; all while encouraging best-practice software engineering when developing models .
DESCRIPTION The Amazon Payments Team manages all Amazon branded payment offerings globally. These offerings are growing rapidly and we are continuously adding new market-leading features and launching new products. Our team manages a financial services machine learning ad serving platform (Billions of impressions per year) through Amazons purchase path where we offer Amazon branded and non-branded payment products and services. Our team of high caliber software developers, data scientists, statisticians and product managers use rigorous quantitative approaches to ensure that we target the right product to the right customer at the right moment, managing tradeoffs between click through rate, approval rates and lifetime value. In order to accomplish this we leverage the wealth of Amazons information to build a wide range of probabilistic models, set up experiments that ensure that we are thriving to reach global optimums and leverage Amazons technological infrastructure to display the right offerings in real time.
AWS and Microsoft may be arch rivals when it comes to competing for business in cloud storage and services, but when it comes to breaking ground in newer areas where volumes of data make a difference to how well the services work and creating systems that are easier to use, collaboration is key. Today, the two companies announced a new deep learning interface called Gluon, designed for developers of all abilities (not just AI specialists) to build and run machine learning models for their apps and other services. Gluon is one of the big steps ahead in taking out some of the grunt work in developing AI systems by bringing together training algorithms and neural network models, two of the key components in a deep learning system. "The potential of machine learning can only be realized if it is accessible to all developers. Today's reality is that building and training machine learning models requires a great deal of heavy lifting and specialized expertise," said Swami Sivasubramanian, VP of Amazon AI, in a statement.