“The Microsoft Fabric implementation program gave our data engineering team a structured path from legacy pipelines to a modern lakehouse architecture.”
Enterprise Program Brief
NVIDIAAdvanced
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.
Duration
8 hours
Level
Advanced
Format
Virtual, On-site, or Hybrid
Language
English
Executive brief for stakeholdersDownload Program Brief (PDF)
Enterprise Track
NVIDIA
Efficient WorkflowsDX
Diagnostics, optimization, throughput
NVIDIA Data Science
FLOW
On this page
Ideal for
Senior Data ScientistData ScienceTailored Team DeliveryImplementation-Focused
Audience Profile
Built for these roles
Designed for experienced data science teams that want more efficient accelerated workflows, better diagnostics, and stronger operational performance at scale.
Overview
Executive overview
Official NVIDIA DLI program focused on building efficient, diagnostic-driven, hardware-accelerated machine learning workflows for large datasets.
Readiness
Prerequisites
- Basic knowledge of a standard data science workflow on tabular data.
- Knowledge of distributed computing using Dask.
- Completion of Fundamentals of Accelerated Data Science or equivalent experience with cuDF and cuML.
Program Outcomes
Capabilities your teams will gain
Create end-to-end accelerated machine learning workflows for large datasets
Use diagnostics to identify delays and workflow bottlenecks
Improve efficiency across data preparation, training, and inference stages
Make more informed platform decisions for high-volume analytics workflows
Curriculum
Curriculum roadmap
1
Accelerated ML pipeline architecture
2
Diagnostic tooling and bottleneck analysis
3
Efficient feature and training workflows
4
Inference and workflow optimization
1Module 1
Design more efficient accelerated ML workflows
+
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
+
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
Delivery Models
Delivery models
Virtual ILTOnsiteHybridExecutive WorkshopBootcampWeekend
Engagement Fit
Engagement fit
Implementation-focused labsPrivate cohort deliveryAdvanced practitioner depthBusiness outcome alignment
Enterprise Customization
Enterprise customization
Tailor this program to your organization's priorities: Helps mature data science teams improve throughput, diagnose delays, and reduce inefficiencies across large-scale machine learning workflows.
- •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
Credentials
Certification & official source
Aligned to the official source referenced for this program.
View Official SourceResources
Program resources
Yes. Most enterprise clients prefer private delivery scoped to role mix, timezone, and rollout timeline. We align lab environments and scenarios to your tenant context where applicable.
Enterprise Proof
Trusted delivery outcomes
“We needed a partner who understood both the technical depth of Azure OpenAI and the governance requirements of an enterprise.”
Delivery Capability
Enterprise-grade instruction
MCT-led delivery
Programs led by Microsoft Certified Trainer practitioners
Enterprise program oversight
Founder-led specialist delivery with structured rollout planning
Global delivery
APAC · EMEA · Americas · Virtual & Onsite
Implementation-focused
Hands-on labs aligned to production scenarios
