Projets AI/ML Ops | GitScrum
Gérez projets MLOps avec GitScrum. Suivez développement modèles, coordonnez déploiements et monitorer ML en production. +50% rapidité.
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How to use GitScrum for AI/ML Ops projects?
Manage MLOps in GitScrum with model-specific labels, track experiment-to-production pipeline, and document model decisions in NoteVault. Coordinate data, training, and deployment work. MLOps teams with structured workflow deploy models 50% faster [Source: MLOps Research 2024].
MLOps workflow:
Data - Data preparation Experiment - Model development Evaluate - Model validation Package - Model packaging Deploy - Model serving Monitor - Production monitoring Retrain - Model updates
MLOps labels
| Label | Purpose |
|---|
| type-mlops | MLOps work |
| ml-data | Data pipeline |
| ml-training | Model training |
| ml-experiment | Experiment |
| ml-deployment | Model deployment |
| ml-monitoring | Production monitoring |
| ml-retraining | Model update |
MLOps columns
| Column | Purpose |
|---|
| Backlog | Planned work |
| Data Prep | Data tasks |
| Experiment | Active experiments |
| Evaluation | Model validation |
| Deployment | Production release |
| Monitoring | Live models |
NoteVault MLOps documentation
| Document | Content |
|---|
| Model catalog | All models |
| Experiment log | Experiments tried |
| Deployment runbook | How to deploy |
| Monitoring guide | What to watch |
| Retraining criteria | When to update |
Experiment task template
## Experiment: [name]
### Hypothesis
If we [change], then [metric] will improve because [reason].
### Data
- Dataset: [name/version]
- Split: train/val/test
- Features: [key features]
### Model
- Architecture: [description]
- Hyperparameters: [key params]
### Results
- Primary metric: [value]
- Secondary metrics: [values]
- Comparison to baseline: [%]
### Outcome
[ ] Promote to production
[ ] Iterate
[ ] Abandon
### Notes
[Key learnings]
Model deployment task
## Deploy Model: [name] v[X]
### Model Info
- Version: [version]
- Experiment: [link]
- Performance: [metrics]
### Deployment
- Environment: [prod/staging]
- Serving: [method]
- Resources: [CPU/GPU/memory]
### Checklist
- [ ] Model validated
- [ ] A/B test configured
- [ ] Monitoring setup
- [ ] Rollback tested
- [ ] Alerts configured
### Rollout
- [ ] 5% traffic
- [ ] 25% traffic
- [ ] 50% traffic
- [ ] 100% traffic
### Rollback Plan
[How to revert]
Data pipeline tasks
| Task Type | Purpose |
|---|
| Data collection | New data sources |
| Data validation | Quality checks |
| Feature engineering | New features |
| Data versioning | Dataset versions |
Training pipeline
| Stage | Tasks |
|---|
| Preprocessing | Data prep |
| Training | Model training |
| Validation | Hold-out testing |
| Hyperparameter | Tuning experiments |
Model monitoring
| Metric | Alert |
|---|
| Prediction latency | > threshold |
| Error rate | > baseline |
| Data drift | Distribution change |
| Model drift | Performance decay |
Retraining triggers
| Trigger | Action |
|---|
| Performance decay | Scheduled retraining |
| New data | Incremental training |
| Data drift | Model update |
| Manual | Ad-hoc retraining |
MLOps metrics
| Metric | Track |
|---|
| Experiment velocity | Experiments per week |
| Deployment frequency | Models per month |
| Model performance | Business metrics |
| Incident rate | Model failures |
Common MLOps issues
| Issue | Solution |
|---|
| Slow experiments | Parallelization |
| Deployment failures | Staging testing |
| Model drift | Monitoring |
| Data quality | Validation pipeline |
Model versioning
| Element | Track |
|---|
| Model artifacts | Model files |
| Training data | Dataset version |
| Code | Git commit |
| Config | Hyperparameters |
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