Item: NVIDIA RAPIDS
Rating: 10
Author: Mauro Bennici

Use Cases and Deployment Scope

NVIDIA RAPIDS helps us create an integrated pipeline process to monitor, design, and deploy deep learning models internally and externally. It is also easy to deploy our services on-premises in our customer's datacenters. The integrated visualization "area" is excellent for instantly running and using detailed data. NVIDIA RAPIDS allows us to be focused on creating value for our customers, avoiding reinventing the wheel.

Pros and Cons

Visualization
Deep learning pipeline
State of the art libraries

GPU restart failure is a tricky bug

Return on Investment

Efficient way to complete tasks
De-facto GPUs standard

Key Insights

Do you think NVIDIA RAPIDS delivers good value for the price?

Yes

Are you happy with NVIDIA RAPIDS's feature set?

Yes

Did NVIDIA RAPIDS live up to sales and marketing promises?

Yes

Did implementation of NVIDIA RAPIDS go as expected?

Yes

Would you buy NVIDIA RAPIDS again?

Yes

Likelihood to Recommend

NVIDIA RAPIDS is great for integrated and planned machine learning and deep learning journey. It is excellent if you have big data with defined processes to be improved and monitored. It is less effective if the project is continuously changed and the data are to be prepared and cleaned a lot and [in] many different ways.

NVIDIA RAPIDS AI

Overall Satisfaction with NVIDIA RAPIDS