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End-to-End MLOps for Crash Simulation Applications

30 Chapters
View Course Samples Online
01
MLOps for crash simulation
unique challenges, lifecycle, and business impact.
MLOpssimulation
02
Crash simulation data landscape
sources, formats, and preprocessing for ML.
simulationlandscape
03
Feature engineering for crashworthiness
from CAE outputs to ML-ready datasets.
crashworthinessCAE
04
Surrogate modeling for crash simulation
when and how to replace expensive solvers.
Surrogatemodeling
05
Experiment tracking and reproducibility in crash simulation ML projects.
Experiment tracking and reproducibility in crash simulation ML projects.
Experimenttracking
06
Versioning datasets, models, and simulation configurations for crash applications.
Versioning datasets, models, and simulation configurations for crash applications.
Versioningdatasets
07
Building a training pipeline for crash prediction models using MLOps tools.
Building a training pipeline for crash prediction models using MLOps tools.
Buildingtraining
08
Hyperparameter tuning and model selection for crashworthiness metrics.
Hyperparameter tuning and model selection for crashworthiness metrics.
Hyperparameterselection
09
Model evaluation for crash simulation
metrics, validation, and uncertainty.
evaluationsimulation
10
Deploying crash prediction models as REST APIs for engineering teams.
Deploying crash prediction models as REST APIs for engineering teams.
Deployingprediction
11
Batch inference and integration with existing CAE workflows.
Batch inference and integration with existing CAE workflows.
inferenceintegration
12
Real-time inference for crash simulation
constraints and optimization.
Real-timeinference
13
Monitoring model performance and data drift in production crash applications.
Monitoring model performance and data drift in production crash applications.
Monitoringperformance
14
Automated retraining and continuous improvement for crash models.
Automated retraining and continuous improvement for crash models.
Automatedretraining
15
CI/CD for ML models in crash simulation
testing, validation, and rollout.
CI/CDsimulation
16
Infrastructure as code for MLOps in crash simulation environments.
Infrastructure as code for MLOps in crash simulation environments.
InfrastructureMLOps
17
Containerization and orchestration of crash simulation ML pipelines.
Containerization and orchestration of crash simulation ML pipelines.
Containerizationorchestration
18
Scalable training on HPC and cloud for large crash datasets.
Scalable training on HPC and cloud for large crash datasets.
Scalabletraining
19
Data governance, security, and compliance in automotive crash ML.
Data governance, security, and compliance in automotive crash ML.
governancesecurity
20
Collaboration and handoff between simulation engineers and data scientists.
Collaboration and handoff between simulation engineers and data scientists.
Collaborationsimulation
21
Case study
end-to-end MLOps for a frontal crash surrogate model.
end-to-endMLOps
22
Case study
MLOps for side impact and occupant safety prediction.
MLOpsoccupant
23
Case study
deploying a crash severity classifier in a cloud environment.
deployingseverity
24
Integrating MLOps with commercial CAE tools and solvers.
Integrating MLOps with commercial CAE tools and solvers.
IntegratingMLOps
25
Handling imbalanced and sparse crash data with MLOps techniques.
Handling imbalanced and sparse crash data with MLOps techniques.
Handlingimbalanced
26
Explainability and trust in crash simulation ML models.
Explainability and trust in crash simulation ML models.
Explainabilitysimulation
27
Performance benchmarking and cost optimization for crash MLOps.
Performance benchmarking and cost optimization for crash MLOps.
Performancebenchmarking
28
Edge deployment of crash models for rapid design iterations.
Edge deployment of crash models for rapid design iterations.
deploymentiterations
29
Future trends
AI-driven crash simulation and autonomous MLOps.
AI-drivensimulation
30
Capstone project
building an end-to-end MLOps pipeline for a crash simulation use case.
Capstonebuilding