Program Outline
Enhancing Data Science Outcomes With Efficient Workflows
This NVIDIA DLI program teaches teams how to create an end-to-end, hardware-accelerated machine learning pipeline for large datasets. It emphasizes diagnostics, workflow analysis, and mitigation of common performance pitfalls across the development lifecycle.
Delivery
Virtual, On-site, or Hybrid
Duration
8 hours
Product
NVIDIA RAPIDS, Triton Inference Server
Role
Senior Data Scientist
NVIDIA
Efficient WorkflowsDiagnostics, optimization, throughput
NVIDIA Data Science
Best Fit
Audience Profile
Who This Program Is For
Designed for experienced data science teams that want more efficient accelerated workflows, better diagnostics, and stronger operational performance at scale.
Overview
Program Summary
Official NVIDIA DLI program focused on building efficient, diagnostic-driven, hardware-accelerated machine learning workflows for large datasets.
Course Outline
Complete Module Sequence
Review the full module sequence for this program, including the primary topic coverage in each module where available.
1Module 1
Design more efficient accelerated ML workflows
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Module 1
Design more efficient accelerated ML workflows
Structure end-to-end workflows for large datasets using GPU-aware tools, libraries, and platform decisions.
- Accelerated ML pipeline architecture
- Efficient feature and training workflows
2Module 2
Diagnose and improve workflow bottlenecks
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Module 2
Diagnose and improve workflow bottlenecks
Use diagnostic techniques and performance analysis to identify delays and improve development-to-inference efficiency.
- Diagnostic tooling and bottleneck analysis
- Inference and workflow optimization
Coverage Areas
Topic Coverage
Coverage Item 1
Accelerated ML pipeline architecture
Coverage Item 2
Diagnostic tooling and bottleneck analysis
Coverage Item 3
Efficient feature and training workflows
Coverage Item 4
Inference and workflow optimization
Customization
Adapt This Program for Your Team
We can adapt this program around your team structure, platform priorities, delivery goals, and the scenarios your people need to work through in practice.
- •Use your largest workflow pain points and representative datasets
- •Add model serving and deployment optimization follow-on sessions
- •Extend into platform observability and operational tuning practices
