At a high level, enterprise AI feigns tasks previously only associated with the human mind, including reasoning, correcting, perceiving and problem-solving. But on the ground, it's central to companies' daily operations, churning through data for business insights, seeing and recognizing images, performing high-volume tasks with great efficiency, and enabling machines to learn without explicit programming.
At the core of these operations and data-intensive workloads is an AI infrastructure consisting of powerful hardware components, software and services. Assembling or enhancing this infrastructure, both in the cloud and on premises, requires a robust AI strategy from both IT and the business.
Storage is a top priority, specifically scalability as data volumes grow. Capacity, IOPS and reliability are also major factors. Networking is another consideration, as AI, specifically deep learning algorithms, requires high-bandwidth and low-latency architectures.
Enterprises also need powerful compute resources, including specialized and efficient CPUs and GPUs. Data prep, cleansing, access and governance also play a big role in enterprise AI initiatives. Finally, AI skills training, development, and recruitment are major considerations for enterprise AI projects.
Maintaining AI infrastructure and getting the most business value from an AI initiative requires data analysts and scientists, developers, security experts, and networking engineers with specific and advanced skills.
AI initiatives touch many parts of enterprise IT, and the market includes enterprise technology mainstays like Intel, IBM, SAP and Microsoft, as well as new power players like Nvidia and Amazon, to name a few. Because few IT endeavors rival the scope of an AI initiative, honing in on the various elements can be challenging for even the most seasoned IT shop.
In this guide, we break down enterprise AI and AI infrastructure, from defining the category and its practical uses to all the considerations for building and maintaining the infrastructure and engine that support the initiatives. Finally, learn about the products and players to inform your enterprise buying decision.
What is enterprise artificial intelligence?
Let's define AI, starting with old extract, transform and load technologies that form the foundation of an artificial intelligence infrastructure by integrating and processing vast amounts of data.
The promise of enterprise AI is built on old ETL technologies, and it relies on an AI infrastructure effectively integrating and processing loads of data. Continue Reading
2Do I need-
Enterprise artificial intelligence, what is it good for?
AI in healthcare, AI in marketing, AI in IT, AI in logistics ... Artificial intelligence touches every part of the enterprise. Learn how your company can benefit from an artificial intelligence infrastructure.
AI in healthcare improves patient outcomes. AI in IT aids employee compliance and security. AI in logistics, AI in marketing, AI in finance -- learn how your company can use AI. Continue Reading
3How to buy-
What to consider when building an AI infrastructure
Building an artificial intelligence infrastructure? Better make sure your storage, networking and data management strategies are sound. Getting the most out of enterprise AI requires deliberate and calculated planning.
Building an artificial intelligence infrastructure requires a serious look at storage, networking and AI data needs, combined with deliberate and strategic planning. Continue Reading
4Top product overviews-
The AI infrastructure market, vendors and products
Major tech vendors offer a vast array of products to build an artificial intelligence infrastructure. Here's a quick snapshot of the products you can use to create an effective AI infrastructure and to get the most out of your AI initiatives.
The artificial intelligence infrastructure market is young and varied, with enterprise AI vendors offering everything from cloud services to powerful, and expensive, hardware. Continue Reading