What is our primary use case?
Our team actively engages in computer vision, data science, and image recognition. Our primary focus lies in harnessing artificial intelligence, particularly in applied mathematics. On one front, our efforts are dedicated to producing AI-driven outcomes in the field of sound. At the same time, we have experts conducting biological experiments and research with AI applications.
How has it helped my organization?
The ease of use is a significant advantage. With a wealth of internet articles and readily available knowledge, there are extensive resources on how to seamlessly integrate various AI and machine learning processes. This accessibility allows our students to quickly and effectively utilize these tools.
What is most valuable?
The cost advantage is paramount. The affordability factor, specifically, the expenses associated with on-premises infrastructure, are five times lower than those incurred in the cloud.
What needs improvement?
I believe there should be an effort to lower costs, especially considering the higher price of the latest update. The focus should be on fostering interactive learning experiences by offering Internet or YouTube workshops and providing educational materials that would help to simplify the learning curve for students.
For how long have I used the solution?
I have been working with it for almost five years.
What do I think about the stability of the solution?
When comparing NVIDIA and HPE, it's evident that NVIDIA is more stable. HPE, in particular, experiences numerous stability issues with its hub.
What do I think about the scalability of the solution?
Scalability remains consistent because both utilize a common approach. Whether scaling out NVIDIA or HPE, the process remains unchanged. We have around two hundred monitors.
How are customer service and support?
NVIDIA support outperforms HPE, although it comes at a higher cost.
Which solution did I use previously and why did I switch?
In terms of price, NVIDIA is approximately twice as expensive as HPE. However, the drawbacks include issues with technical support and the less mature ecosystem of HPE. NVIDIA, on the other hand, excels in investing significant efforts in deploying a robust AI and machine learning ecosystem and community.
How was the initial setup?
The initial setup was highly complex, prompting our reliance on partners, hardware vendors, and integrators to ensure a well-designed and properly deployed system. It's not a straightforward process; considerable energy and effort are required to establish a fully functional AI infrastructure.
What about the implementation team?
The deployment process begins with a thorough understanding of the business case and requirements set by our scientists. Translating these business needs into system requirements involves a careful selection of interconnects, storage providers, and server providers. Choosing partners to integrate these components effectively is crucial in creating a functional puzzle. Then, we define acceptance criteria, including specific test cases and stress tests, to ensure the seamless operation of all components. Real-life user cases are then introduced to evaluate the performance, comparing results with other systems to validate the efficacy of our deployment. For a single lab deployment, this process typically takes around a month. However, for larger institutional or departmental deployments, involving about eight to ten team members, the timeframe extends to approximately six months. Maintaining traditional infrastructure is a complex task that demands skilled professionals with significant expertise in AI infrastructure and machine learning.
What was our ROI?
Regarding ROI, our focus is not solely on monetary gains, as we operate as an academic institution. Instead, we gauge our investments' success by generating highly qualified academic articles published in prestigious journals such as Nature, Science, and Life Science. This serves as our metric of success.
What's my experience with pricing, setup cost, and licensing?
Generally, the price is affordable, but the most recent update comes with a notable increase in cost.
What other advice do I have?
I advise those entering the AI field to be cautious and seek guidance from experienced professionals. It's crucial to approach such decisions thoroughly understanding business goals and organizational objectives. Overall, I would rate it eight out of ten.
Which deployment model are you using for this solution?
On-premises
Disclosure: My company has a business relationship with this vendor other than being a customer: Integrator