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Obstacles to AI & Analytics Adoption in The Cloud

Trifacta launched a benchmark report in conjunction with Researchscape: Obstacles to AI & Analytics Adoption in the Cloud, to examine how data workers in the U.S. across industries are handling the increased move of data to the cloud, the time constraints endured when preparing data for analytics, artificial intelligence and machine learning initiatives, and the impact these obstacles have on the overall success of these projects.

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.

Ethical AI: Five Guiding Pillars

Corporate responsibility is not a new mission, but it has become a more complicated one as machine learning assumes a larger role in how work is done. This 20 page white paper provides five actionable ways organizations can re-imagine business models around ethical AI, according to KPMG.

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 […]

AI-Driven Data Catalogs for the Insights-Driven Enterprise: How to Find the Right Data Catalog

Data lakes have turned into data swamps. Metadata initiatives have derailed. As a result, data discovery and retrieval are ongoing, head-banging challenges. In the universal search for a workable solution, data catalogs are quickly rising to the top of the list. Download the new report from Io-Tahoe that outlines some of the major considerations to help you find the AI-driven data catalog that will work best for your organization. 

Intelligent Sensing: The Impact of AI on Sensor Capabilities

The rise of artificial intelligence (AI) is unlocking a wave of new sensor applications and driving market demand for intelligent sensing – the ability to extract insights from sensor data. To guide innovation and investment in this fast-evolving market, the team at Lux Research, a leading provider of tech-enabled research and advisory services for technology innovation, took a deep dive into how and where enhanced AI analytics are rapidly improving the […]

Alternative Data for Investment Management

From hedge fund managers to mutual funds and even private equity managers, alternative data has the power to improve valuation of securities and boost the clarity of the investment process.  Techniques like natural language processing and machine learning allow organizations to better capitalize on alternative data. These technologies enable processing of large, heterogenous, and unstructured sets at an extremely fast rate. A new report from SparkCognition explores the challenges for alternative data adoption, how to overcome them, and explores the potential of automation.