Program Outline
Applications of AI for Predictive Maintenance
Covers anomaly and failure identification in time-series data and mapping those signals to predictive maintenance decisions.
Delivery
Virtual, On-site, or Hybrid
Duration
8 hours
Product
TensorFlow, Keras, RAPIDS
Role
Data Scientist
NVIDIA
Deep LearningApplications of AI for Predictive Maintenance
TensorFlow, Keras, RAPIDS
Best Fit
Audience Profile
Who This Program Is For
Built for teams exploring AI-led maintenance and failure prediction workflows.
Overview
Program Summary
Official NVIDIA DLI workshop on anomaly and failure detection in predictive maintenance workflows.
Program Outcomes
After this program, participants will be able to...
Model failure conditions from time-series data
Apply deep learning to predictive maintenance scenarios
Delivery Formats
Enterprise Suitability
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
Apply AI to maintenance scenarios
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Module 1
Apply AI to maintenance scenarios
Use time-series deep learning patterns to identify failures and estimate useful life.
- Time-series anomaly detection
- Remaining useful life modeling
Coverage Areas
Topic Coverage
Coverage Item 1
Time-series anomaly detection
Coverage Item 2
Remaining useful life modeling
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 operational equipment scenario
- •Add deployment and monitoring workflows
