Sign up for our newsletter and get the latest big data news and analysis.

The 3 Reasons Enterprises Need an AI Operating System for Intelligent process Automation

This new whitepaper from Veritone highlights how evolving technology meets enterprise demand for agile, intelligence-based solutions in the shape of AI-based operating systems (OS) across three areas: (i) AI OS for automation of human work; (ii) AI OS for process automation across all data sources; and (iii) AI OS for democratization of AI across the
enterprise.

Reduce Compliance Risk in Your Call and Contact Center With AI

This white paper from Veritone discusses how compliance represents a major risk for contact centers, with violations potentially costing organizations millions of dollars in fines and causing reputational damage to the brand. The current method of monitoring calls — using manual review of recorded conversations — does not come close to meeting the requirements for comprehensive monitoring of regulated interactions. AI text-to-speech technology provides the solution to this dilemma, with its capability to transcribe, translate, redact, find objects and generate searchable indexes of limitless hours of recorded calls in near real-time.

Autonomous Enterprise: The Case for AIOps

Digitate, a leading autonomous enterprise software provider, announced the results of its inaugural Autonomous Enterprise survey. Digitate surveyed leading European AIOps influencers in September 2020. The report aimed to better understand how Artificial Intelligence (AI)-enabled Intelligent IT Operations and automation are impacting IT Infrastructure, applications, end-user services for European companies, and the barriers that might be preventing them from realizing its full potential.

Data Engineering Survey: 2021 Impact Report

This Data Engineering Survey: 2021 Impact Report summarizes key findings from the inaugural survey and provides a glimpse into the current and future state of data engineering and DataOps. The report highlights some of the major trends uncovered in this year’s survey including the adoption of cloud data platforms, what platforms are winning (and emerging), what data engineers find to be their biggest challenges, and how organizations are handling sensitive data.

XAI: Are We Looking Before We Leap?

This four part report from our friends over at SOSA highlights the challenges and advances when it comes to regulations, relevant use-cases, and emerging technologies taking the mystery out of AI.

Driving ROI Through AI

This new report from ESI ThoughtLab was conducted alongside our friends over at DataRobot as well as a coalition of other AI leaders. The report shows that despite high adoption rates of AI in enterprises, ROI in AI still remains a work in progress and will take skill, scale, and time.

Be (More) Wrong Faster – Dumbing Down Artificial Intelligence with Bad Data

In this white paper, our friends over at Profisee discuss how Master Data Management (MDM) will put your organization on the fast track to automating processes and decisions while minimizing resource requirements, while simultaneously eliminating the risks associated with feeding AI and ML data that is not fully trusted. In turn, your digital business transformation will be accelerated and your competitive edge will be rock solid.

The Real AI Revolution: Machines That Learn Like Scientists

In this compelling white paper, our friends over at causaLens highlight how ML has wrongly become synonymous with AI. We must shake off this misconception to start  the real AI revolution. Data science must forgo its reliance on curve-fitting ML and return to its roots; to put the science back into data science. causaLens is a major contributor to this new science of causality. And it is the company’s mission to help  organizations of all types to benefit from it.

Interview: Global Technology Leader PNY

The following whitepaper download is a reprint of the recent interview with our friends over at PNY to discuss a variety of topics affecting data scientists conducting work on big data problem domains including how “Big Data” is becoming increasingly accessible with big clusters with disk-based databases, small clusters with in-memory data, single systems with in-CPU-memory data, and single systems with in-GPU-memory data. Answering our inquiries were: Bojan Tunguz, Senior System Software Engineer, NVIDIA and Carl Flygare, NVIDIA Quadro Product Marketing Manager, PNY.

The Future Starts Now – Achieving Successful Operation of ML & AI-Driven Applications

Operationalizing AI and ML has become an unavoidable need in business, as various industries heavily rely on large volumes of real-time data as input to automated decision-making processes to yield the best results. Use cases in the data science field have shown that ML models and AI have few tangible business benefits until they are operationalized. In this e-book, our friends over at MemSQL show us how to successfully deploy model-driven applications into production.