Deployment & Scaling · Practitioner
RAPIDS: GPU-Accelerated Dataframes and Classic ML
Most enterprise AI time is spent on tabular data, not transformers. RAPIDS puts pandas, scikit-learn, NetworkX and Spark workloads on the GPU — cuDF's pandas accelerator mode needs no code changes at all.
- 1.3 hours lesson
- intermediate level
- NVIDIA AI Track
- Narrated presentation
What you’ll learn
- cudf.pandas accelerator mode with automatic CPU fallback and no code changes
- Speedups scale with data size; small frames can be slower than CPU
- cuML for scikit-learn-compatible classic ML, cuGraph for NetworkX, cuVS for vector search
- Spark RAPIDS for accelerating existing Spark stages
- Dask-cuDF and chunking when data exceeds GPU memory
- Choosing RAPIDS by bottleneck, not by enthusiasm
Inside this lesson
A narrated, slide-by-slide presentation with 1 prediction exercise, 1 knowledge check, 3 videos. You answer as you go, and the platform checks your work.