Machine Learning Projects | 50% Faster Iterations

Manage ML projects with GitScrum experiment tracking and model lifecycle management. Coordinate data science and engineering teams. 50% faster iterations.

4 min read

How to use GitScrum for Machine Learning projects?

Manage ML projects in GitScrum by tracking experiments as tasks, using labels for model types and stages, and documenting results in NoteVault. Coordinate data preparation, model training, and deployment through visual workflow. Teams with structured ML workflows iterate 50% faster [Source: MLOps Research 2024].

ML project workflow:

Research - Problem definition

Data prep - Collection, cleaning

Experimentation - Model development

Evaluation - Performance testing

Productionize - Engineering handoff

Deploy - Model serving

Monitor - Performance tracking

ML labels

Label	Purpose
stage-research	Problem exploration
stage-data	Data work
stage-training	Model training
stage-evaluation	Testing
stage-deployment	Production
model-[type]	Model type
experiment	Experiment tracking

ML columns

Column	Purpose
Backlog	Ideas, requirements
Research	Problem analysis
Data Prep	Data collection/cleaning
Training	Model development
Validation	Performance evaluation
Staging	Pre-production
Production	Deployed models

Experiment task template

## Experiment: [name]

### Hypothesis
What we expect to learn/achieve

### Parameters
- Model: [type]
- Dataset: [name]
- Features: [list]
- Hyperparameters: [values]

### Results
- Accuracy: [metric]
- Other metrics: [values]
- Observations: [notes]

### Conclusion
Keep/discard and why

NoteVault ML documentation

Document	Content
Model registry	All models, versions
Data catalog	Datasets, features
Experiment log	All experiments
Model cards	Model documentation
Deployment guides	How to deploy

Data science vs engineering

Team	Tasks
Data Science	Research, experiments, evaluation
ML Engineering	Productionization, deployment
Data Engineering	Data pipelines
DevOps	Infrastructure, MLOps

Experiment tracking

Field	Record
Parameters	Model config
Metrics	Performance measures
Artifacts	Model files
Code	Experiment version
Data	Dataset version

Model lifecycle stages

Stage	Tasks
Development	Experiments, prototypes
Validation	A/B tests, holdout tests
Canary	Limited production
Production	Full deployment
Retired	Replaced/removed

MLOps workflow

Phase	GitScrum Tracking
CI	Training pipeline tasks
CD	Deployment tasks
CT	Continuous training tasks
Monitoring	Performance tasks

Common ML project issues

Issue	Solution
Lost experiments	Experiment label, documentation
Handoff failures	Linked tasks DS ↔ Eng
Model drift	Monitoring tasks
Data issues	Data quality tasks

ML team metrics

Metric	Track
Experiments run	Task count
Models deployed	Deployment tasks
Iteration time	Cycle time
Model performance	NoteVault tracking