ML Project Development Template

A structured framework for building end-to-end machine learning projects on the Domino Data Lab platform

What You'll Build

This template guides you through creating a complete, production-ready ML solution with:

• Automated data pipelines with quality validation
• Advanced ML models with comprehensive experimentation
• Governance compliance across multiple frameworks
• Interactive dashboards for stakeholder engagement
• Production deployment with monitoring and CI/CD
• Full documentation and reproducibility

Project Lifecycle

Phase 1: Business Understanding (e001)

Define the problem, success metrics, and governance requirements

• Stakeholder requirement analysis
• Success criteria definition
• Governance framework identification
• ROI projections

Phase 2: Data Engineering (e002)

Acquire, generate, or connect to your data sources

• Data pipeline creation
• Quality validation
• Synthetic data generation (if needed)
• Data versioning and lineage

Phase 3: Exploratory Analysis (e003)

Understand your data and extract actionable insights

• Statistical analysis
• Feature discovery
• Visualization creation
• Hypothesis formation

Phase 4: Model Development (e004)

Build and optimize machine learning models

• Algorithm selection
• Hyperparameter tuning
• Cross-validation
• MLflow experiment tracking

Phase 5: Model Validation (e005)

Ensure model quality, fairness, and compliance

• Performance testing
• Bias detection
• Robustness validation
• Governance compliance checks

Phase 6: Deployment Pipeline (e006)

Create production-ready deployment infrastructure

• API development
• Docker containerization
• CI/CD pipeline setup
• Monitoring configuration

Phase 7: User Interface (e007)

Build interactive applications for model consumption

• Dashboard creation
• Real-time predictions
• Business metrics tracking
• User feedback loops

Quick Start

Complete Project Generation

Simply describe what you want to build:

"Create a customer churn prediction system with real-time scoring"

This single command will:

1. ✅ Generate synthetic customer data
2. ✅ Perform comprehensive EDA
3. ✅ Train multiple ML models
4. ✅ Validate for bias and fairness
5. ✅ Deploy production API
6. ✅ Create interactive dashboard
7. ✅ Set up monitoring

Targeted Development

Focus on specific phases of your project:

# Start with data
"Generate synthetic financial transaction data for fraud detection"

# Focus on modeling
"Build an ensemble model for this dataset optimizing for precision"

# Create visualization
"Build a Streamlit dashboard showing model predictions and explanations"

Project Structure

Your Project/
├── e001-business-analysis/     # Requirements & governance
├── e002-data-wrangling/        # Data pipelines & quality
├── e003-data-science/          # EDA & insights
├── e004-model-development/     # ML training & optimization
├── e005-model-validation/      # Testing & compliance
├── e006-mlops/                 # Deployment & monitoring
└── e007-frontend/              # Applications & dashboards

Each phase produces:

• 📝 Production-ready code
• 📊 Comprehensive artifacts
• 📦 Dependencies (requirements.txt)
• 📈 MLflow tracking
• ✅ Validation reports

Example Projects

Credit Risk Assessment

Industry: Financial Services Complexity: High Governance: NIST RMF, Model Risk Management

"Build a credit risk model with explainability and regulatory reporting"

Deliverables:

• Risk scoring API
• Fairness validation report
• Model explainability dashboard
• Regulatory compliance documentation
• A/B testing framework

Customer Lifetime Value

Industry: E-commerce Complexity: Medium Governance: GDPR, Ethical AI

"Create a CLV prediction system with customer segmentation"

Deliverables:

• Segmentation analysis
• Value prediction models
• Marketing automation integration
• ROI calculator
• Performance monitoring

Demand Forecasting

Industry: Retail Complexity: Medium Governance: Business Continuity

"Develop a demand forecasting system with inventory optimization"

Deliverables:

• Time series models
• Inventory recommendations
• Supply chain dashboard
• Alert system
• What-if analysis tools

Technology Stack

Core Technologies

• Python 3.8+ - Primary development language
• MLflow - Experiment tracking and model registry
• Docker - Containerization for deployment
• Git - Version control

ML Frameworks

• scikit-learn - Classical ML algorithms
• XGBoost/LightGBM - Gradient boosting
• TensorFlow/PyTorch - Deep learning
• statsmodels - Statistical modeling

Deployment & UI

• FastAPI - High-performance APIs
• Streamlit - Quick interactive apps
• Dash/Gradio - Advanced dashboards
• Domino Flows - Workflow orchestration

Governance & Compliance

Built-in support for enterprise governance frameworks:

• ✅ NIST Risk Management Framework
• ✅ Model Risk Management V3
• ✅ Ethical AI Guidelines
• ✅ GDPR/CCPA Compliance
• ✅ SOX Controls

Automated compliance features:

• Model intake process
• Approval workflows
• Audit trails
• Performance monitoring
• Drift detection

MLflow Integration

Every project includes comprehensive MLflow tracking:

mlflow.set_experiment("your_project_name")

# Automatic tracking of:
- Parameters (hyperparameters, configs)
- Metrics (accuracy, precision, recall, custom)
- Models (serialized with signatures)
- Artifacts (plots, reports, data samples)
- Tags (version, stage, owner)

Parent-child run hierarchy for complex pipelines:

• Master orchestration run
• Nested stage runs
• Experiment comparison
• Model registry integration

Getting Started

Prerequisites

• Domino workspace access
• Python environment
• MLflow server (optional)

Installation

# Clone this template
git clone <repository>

# Install base dependencies
pip install -r requirements.txt

Your First Project

1. Define your use case

   "I need a model to predict customer churn"

2. Watch the automated workflow

   • Data generation/acquisition
   • Exploratory analysis
   • Model training
   • Validation
   • Deployment

3. Customize as needed

   • Adjust model parameters
   • Add custom features
   • Modify UI components

Advanced Features

Custom Data Integration

"Connect to our Snowflake warehouse and build a sales forecast model"

Ensemble Methods

"Create a stacked ensemble combining XGBoost, Random Forest, and Neural Networks"

Real-time Processing

"Build a streaming anomaly detection system with Kafka integration"

AutoML Capabilities

"Use AutoML to find the best model for this dataset"

Best Practices

Project Planning

• Define clear success metrics upfront
• Identify governance requirements early
• Plan for model monitoring from the start

Development

• Use version control for all code
• Track all experiments in MLflow
• Document assumptions and decisions
• Create reproducible pipelines

Deployment

• Containerize applications
• Implement health checks
• Set up alerting
• Plan for model updates

Support & Resources

Documentation

• Project Setup Guide (coming soon)
• Workflow Examples (coming soon)
• API Reference (coming soon)
• Troubleshooting (coming soon)

Community

• Domino Community Forum
• GitHub Discussions
• Slack Channel

Help

• Email: support@domino.ai
• Documentation: docs.domino.ai
• Issues: GitHub Issues

Contributing

We welcome contributions!

• Code contribution guidelines
• Documentation improvements
• Bug reports and feature requests
• Community examples

License

MIT License

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Accelerate Model Development • Ensure Governance • Deploy with Confidence