How ready is your business to adopt artificial intelligence (AI) quickly?
In our latest eBook, we’re doing a deep dive into how your enterprise can successfully adopt AI by building a high-performance data infrastructure.
Specifically, we break down:
- The rise of AI workloads in the enterprise datacenter
- How you can evaluate your organization’s readiness to adopt AI
- Building high-performing datacenter infrastructure
- Getting started on your path to AI
AI is here, and chances are your competitors are already adopting it. This free resource gives you a solid starting point for your own AI adoption process. Here’s an extended excerpt:
The recent rise of unstructured data has radically changed things. Now, organizations have access to an unprecedented stream of information, the vast majority of which has no immediate purpose.
At the same time, the increased adoption of the public cloud has made storing unstructured data much more cost effective. Rather than continually adding capacity in their own datacenters, IT leaders now have the option to leverage the cloud—or a hybrid approach—to store and put to work entire oceans of data.
These two developments—unstructured data and the cloud—have created entirely new avenues for enterprises. AI, in particular, can add immense value by providing:
- Rapid data analysis that makes it possible for enterprises to make smarter decisions more quickly
- Improved internal communications for better resource management
- Automation of repetitive tasks to reduce human errors and talent burnout
- Better customer service by way of always available chatbots that help alleviate the need for 24x7 support teams
Given these benefits, it’s no wonder more and more enterprises across industries are looking to jump head-first into AI adoption. But adopting the technology is drastically different from successfully putting it to work.
Why? Because in order for AI to be effective, it needs:
- Massive amounts of data to produce models enabling advanced predictions, recommendations, or natural language and image processing;
- High-performance infrastructure to allow those models to be trained and optimized; and
- On-demand infrastructure that supports deploying those models to production and building APIs to tie them into real-world workflows.
You can download your free copy of Putting Artificial Intelligence to Work: A Guide to Designing High-Performance Data Infrastructure for AI Workloads here.
And if you have any more questions about AI—or any other technical needs—contact one of our experts.
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