AIMadeEasy

Fundamentally more data leads to better accuracy and quality of models.

Being able to serve that data with low latency and high throughput is critical so clearly the performance of the technology is important.

The microscopy example given earlier is really telling. Performance yields the ability to get higher resolution images out of the instrument, whether for medical purposes or research. But also leads to real-time analysis as well which can be critical for insight

Real-time is just so essential for so many AI use cases, the I/O can't hold you back when decisions need to be made instantaneously, whether it is in retail, medical or government use cases

I would love to hear what thoughts some of our viewers have!

@DrV_DDN George you are exactly right. Going from 512 x 512 pixels to 4K images creates a critical performance challenge. Plus adding multi-spectral data it gets even more interesting.

The higher the performance the shorter the TAT. Gives the option of doing additional trials on the same hardware.

Working with customers I see a full range of maturity. Starting with people using GPU on the cloud or a couple GPUs in workstation to full production. However it is always key to think about managing the data.

We encounter such a range of customers in the AI adoption journey, from those just looking into it, to others with wide adoption - so many we are talking to now "just don't want to get left behind".

Not directly answering the question, but the easy scalability of the presented solution makes it applicable for those with relatively small amounts of data, but growing with them as they prove out their results and in turn gather more data.

Medical imaging is definitely critical

Medical imaging to me is one of the most important fields for applying deep learning because of its direct impact on outcomes for patients.

Obviously autonomous vehicles require AI and is probably one of the most data intensive cases out there.

Genomic processing!

@darrinpjohnson Totally agree Darrin... AI and the application of deep learning toward medical imaging will immediately impact us as a society and personally.

@darrinpjohnson The application to personalized medicine more broadly is also interesting

@YvonneW6463 There are impressive results for acceleration of genome analysis by Parabricks https://www.ddn.com/...

multi-disciplinary research into AI is fascinating to me, how do the different disciplines learn from each other?

Your exactly right...DDN's nearly 20 years of experience serving the HPC community brings both the experience and the technology critical to Deep Learning.