Most people know machine learning (ML) as the recommendations you see on shopping sites and the song suggestions you see on music services. It’s moved so far into the mainstream, it’s often just called AI, as if that’s all there is to AI.
The new frontier of ML is deep learning (DL), the advanced realm of self-driving cars. However, the infrastructure needs of DL are different from ML hardware. Infrastructure implementations can and do vary but there are some core considerations you need to consider as you build out your system.
Fortunately, modern open-source technology allows highly qualified engineering teams to design highly effective clusters that are also very strong on ROI.
Choosing the AI model determines what data you want to ingress, what tools you use, which components are required, and how those components are connected. Once you’ve selected your AI model, picked your processing framework, and structured your data, you typically want to run a proof of concept (POC) for the learning phase of the project and likely a separate one for the inference portion (as the requirements for each are different), though that is not always possible due to cost. If the POC testing process works out, you want to move to production. Often, a successful program means scaling to gain even more value from your data.
Not every organization is at the same stage of AI adoption. Read this document to better understand the stages of AI-readiness and what level of investment is best suited to where you are in your AI journey.Read Document
Inference and Training both fall in the category of AI but what they are, and the hardware they require, is very different. Read this document to learn some key differences in what makes an inference solution vs. a training solution.Read Document
A powerful system architecture designed from the group up to optimize large AI datasets. This Linux-based cluster design includes best-of-breed technology, including the NVIDIA® HGX™ and AMD EPYC™Learn More
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