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The Essential Guide: Machine Scheduling for AI Workloads on GPUs

This white paper by Run:AI (virtualization and acceleration layer for deep learning) addresses the challenges of expensive and limited compute resources and identifies solutions for optimization of resources, applying concepts from the world of virtualization, High-Performance Computing (HPC), and distributed computing to deep learning.

Machine learning for all: the democratizing of a technology

In this short eBook, you’ll discover automated machine learning using H2O.ai. H2O.ai has dedicated itself to democratizing all aspects of AI, including machine
learning. H2O Driverless AI is a machine learning solution that automates AI for nontechnical
users. So-called “AutoML” solutions like H2O Driverless AI are rising in popularity for enterprises across a wide range of industries. With it, users can build robust, fast, and accurate machine learning solutions. It also includes visualization and interpretability features that explain the data modeling results in plain English, fostering further adoption and trust in AI.

Overcoming Obstacles to Machine Learning Adoption

This is a new Business Impact Brief from 451 Research sponsored by H2O.ai – “Overcoming Obstacles to Machine Learning Adoption.” After many fits and starts, the era of enterprise machine learning has finally arrived. According to 451 Research’s Voice of the Enterprise, AI and Machine Learning survey, 20% of enterprises have already deployed the technology and a further 33% plan to do so within one year.

insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads

This new technology guide from DDN shows how optimized storage has a unique opportunity to become much more than a siloed repository for the deluge of data constantly generated in today’s hyper-connected world, but rather a platform that shares and delivers data to create competitive business value. The intended audience for this important new technology guide includes enterprise thought leaders (CIOs, director level IT, etc.), along with data scientists and data engineers who are a seeking guidance in terms of infrastructure for AI and DL in terms of specialized hardware. The emphasis of the guide is “real world” applications, workloads, and present day challenges.

AI in the Enterprise: Trends & Insights on Vendor Selection and Implementation

A new report was released by our friend over at Leverton, a data extraction startup recently acquired by MRI Solutions, titled “AI in the Enterprise: Trends & Insights on Vendor Selection and Implementation.” The report lends insight into the experiences and preferences of “leaders,” “lookers,” and “laggards” (defined below) when it comes to AI deployment.

DarwinAI Generative Synthesis* Platform and Intel® Optimizations for TensorFlow* Accelerate Neural Networks

DarwinAI, a Waterloo, Canada startup creating next-generation technologies for Artificial Intelligence development, announced that the company’s Generative Synthesis platform – when used with Intel technology and optimizations – generated neural networks with a 16.3X improvement in image classification inference performance. Intel shared the optimization results in this recently published solution brief. The complexity of deep […]

Darwin Efficacy Report: Accelerating Data Science at Scale by Automation

Darwin, a machine learning platform, accelerates data science at scale by automating the building and deployment of models. It provides a productive environment that empowers data scientist with a broad spectrum of experience to quickly prototype use cases and develop, tune, and implement machine learning applications in less time. Download the latest white paper from SparkCognition that compares how Darwin performs against other platforms in the market on the same datasets.

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.

Four Types of Machine Learning Bias

AI models comprise algorithms and data, and they are only as good as their underlying mathematics and the data they are trained on. When things go wrong with AI it’s because either the model of the world at the heart of the AI is flawed, or the algorithm driving the model has been insufficiently or incorrectly trained. Download the new whitepaper from Alegion that can help AI project leads and business sponsors better understand the four distinct types of bias that can affect machine learning, and how each can be mitigated.

The Math Behind Machine Learning

Machine learning is a wildly popular field of technology that is being used by data scientists around the globe. Mastering machine learning can be achieved via many avenues of study, but one arguably necessary ingredient to success is a fundamental understanding of the mathematics behind the algorithms. Some data scientists-in-training often try to take a […]