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Infographic: AI – The Secret to Sustainable Commerce

Artificial intelligence has the ability to streamline business operations by analyzing vast amounts of data to target inefficiencies in the system. The infographic below, courtesy of Noodle.ai, outlines how AI can streamline the supply chain and lead to a more sustainable business and happier customers.

3 Non-Obvious Keys to Being AI-Ready

Data scientists know what they are doing, and most organizations have no cause to worry about the soundness of their machine learning (ML) algorithms. Where AI readiness typically lags is in other parts of the process. In most organizations today, the process of building, deploying and maintaining AI systems bears no resemblance to traditional IT. Alegion explores three key strategies your business can employ to be AI-ready.

Best of arXiv.org for AI, Machine Learning, and Deep Learning – March 2019

In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the month.

Splice Machine Launches ML Manager Beta Program to Meet the Growing Demand for Operational AI

Splice Machine, the operational artificial intelligence (AI) data platform, announced the launch of a beta program for ML Manager, a native data science and machine learning platform. Operating on top of Splice Machine’s data platform, ML Manager empowers data science teams to maximize the performance of their machine learning models by removing the latency associated with building complex data pipelines, performing cumbersome transformations and training models on updated data.

Scaling Production AI

As AI models grow larger and more complex, it requires a server architecture that looks much like high performance computing (HPC), with workloads scaled across many servers and distributed processing across the server infrastructure. Barbara Murphy, VP of Marketing, WekaIO, explores how as AI production models grow larger and more intricate, server architecture gets more complex. Explore how tools like GPU clusters and more are moving the dial forward on AI. 

AI Critical Measures: Time to Value and Insights

AI is a game changer for industries today but achieving AI success contains two critical factors to consider — time to value and time to insights.  Time to value is the metric that looks at the time it takes to realize the value of a product, solution or offering. Time to insight is a key measure for how long it takes to gain value from use of the product, solution or offering.

A Blueprint for Preparing Your Own Machine Learning Training Data

Download the new guide from Alegion that acts as a pre-flight checklist for data science teams that are contemplating preparing their own maching learning training data.

AI Goes Mainstream

According to a recent Gartner survey, Artificial intelligence (AI) learning has moved from a specialized field into mainstream business use with 37 percent of respondents reporting their enterprises either had deployed AI or would do so shortly. WekaIO’s Barbara Murphy explores the path of artificial intelligence from the fringe to mainstream business practices. Find out what is driving AI growth and adoption.

Alegion Outlines the 4 Most Prevalent Types of AI Bias

AI systems are becoming more and more of the norm as machine and deep learning gain grown — especially within the data center and colocation markets. That said, Artificial Intelligence systems are only as good as their underlying mathematics and the data they are trained on. Download a new report from Alegion to further understand the bias behind machine learning and how to avoid four potential pitfalls.

How to Get to the Data-Enabled Data Center

Despite their many promising benefits, advancements in Artificial Intelligence (AI) and Deep Learning (DL) are creating some of the most challenging workloads in modern computing history and put significant strain on the underlying I/O, storage, compute and network. An AI-enabled data center must be able to concurrently and efficiently service the entire spectrum of activities involved in the AI and DL process, including data ingest, training and inference.