AI/ML Development Workflows

Version: 1.0.0 Status: Production Last Updated: December 28, 2025 Category: AI/ML Development

---

Workflow Overview

This document provides a comprehensive library of AI/ML development H.P.006-WORKFLOWS for the CODITECT platform. These H.P.006-WORKFLOWS cover the complete machine learning lifecycle including data preparation, model training, evaluation, deployment, monitoring, and MLOps automation. Each workflow includes detailed phase breakdowns, inputs/outputs, and success criteria to ensure reliable ML operations.

---

Inputs

Input	Type	Required	Description
training_data	object	Yes	Reference to training dataset
model_H.P.009-CONFIG	object	Yes	Model architecture and hyperparameters
experiment_H.P.009-CONFIG	object	Yes	Experiment tracking H.P.009-CONFIGuration
evaluation_metrics	array	Yes	Metrics to evaluate model performance
deployment_H.P.009-CONFIG	object	No	Deployment target H.P.009-CONFIGuration
monitoring_H.P.009-CONFIG	object	No	Production monitoring settings

---

Outputs

Output	Type	Description
model_id	string	Unique identifier for trained model
experiment_id	string	Experiment tracking ID
model_metrics	object	Evaluation metrics (accuracy, F1, AUC, etc.)
model_artifact	string	Path to serialized model artifact
deployment_endpoint	string	Production inference endpoint
monitoring_dashboard	string	Link to model monitoring dashboard

---

Phase 1: Data Preparation & Feature Engineering

Initial phase prepares data for model training:

1. Data Collection - Aggregate data from sources
2. Data Cleaning - Handle missing values, outliers, duplicates
3. Feature Engineering - Create and select features
4. Data Splitting - Train/validation/test splits
5. Data Versioning - Version datasets for reproducibility

---

Phase 2: Model Training & Evaluation

Core phase trains and evaluates ML models:

1. Experiment Setup - Initialize experiment tracking
2. Model Training - Train with hyperparameter optimization
3. Model Evaluation - Evaluate on validation/test sets
4. Model Comparison - Compare with baseline and previous versions
5. Model Selection - Select best performing model

---

Phase 3: Deployment & Monitoring

Final phase deploys models and sets up monitoring:

1. Model Registration - Register model in model registry
2. Deployment - Deploy to inference endpoint
3. A/B Testing - Configure canary/shadow deployments
4. Monitoring Setup - Configure drift and performance monitoring
5. Alerting - Set up performance degradation alerts

---

AI/ML Workflow Library

1. model-training-pipeline-workflow

• Description: Complete supervised learning model training pipeline with experiment tracking
• Trigger: /train-model or manual
• Complexity: complex
• Duration: 30m-4h
• QA Integration: validation: required, review: required
• Dependencies:
  • Agents: ml-engineer, data-scientist
  • Commands: /run-experiment, /validate-model
• Steps:
  1. Data validation - data-scientist - Verify data quality
  2. Feature engineering - ml-engineer - Create features
  3. Model training - ml-engineer - Train with HPO
  4. Experiment logging - ml-engineer - Log to MLflow/W&B
  5. Model validation - data-scientist - Evaluate performance
• Tags: [ml, training, supervised-learning, mlops]

---

2. feature-engineering-workflow

• Description: Systematic feature engineering with feature store integration
• Trigger: /engineer-features or data update
• Complexity: moderate
• Duration: 15-60m
• QA Integration: validation: required, review: recommended
• Dependencies:
  • Agents: ml-engineer, data-scientist
  • Commands: /create-features, /store-features
• Steps:
  1. Feature analysis - data-scientist - Analyze raw features
  2. Feature creation - ml-engineer - Create derived features
  3. Feature selection - data-scientist - Select important features
  4. Feature encoding - ml-engineer - Encode categorical features
  5. Feature store update - ml-engineer - Store in feature store
• Tags: [ml, features, preprocessing, feature-store]

---

3. model-deployment-workflow

• Description: Deploy trained models to production inference endpoints
• Trigger: /deploy-model or model registration
• Complexity: complex
• Duration: 15-30m
• QA Integration: validation: required, review: required
• Dependencies:
  • Agents: ml-engineer, devops-engineer
  • Commands: /deploy-model, /validate-endpoint
• Steps:
  1. Model packaging - ml-engineer - Package model for deployment
  2. Container build - devops-engineer - Build inference container
  3. Endpoint deployment - devops-engineer - Deploy to serving platform
  4. Smoke testing - ml-engineer - Test inference endpoint
  5. Traffic routing - devops-engineer - Route production traffic
• Tags: [ml, deployment, inference, serving]

---

4. model-monitoring-workflow

• Description: Continuous model performance and data drift monitoring
• Trigger: Continuous or scheduled
• Complexity: moderate
• Duration: Continuous
• QA Integration: validation: required, review: recommended
• Dependencies:
  • Agents: ml-engineer, data-scientist
  • Commands: /monitor-model, /detect-drift
• Steps:
  1. Prediction logging - ml-engineer - Log predictions and inputs
  2. Performance tracking - data-scientist - Track accuracy metrics
  3. Drift detection - ml-engineer - Monitor data/concept drift
  4. Alert evaluation - data-scientist - Evaluate alert conditions
  5. Reporting - ml-engineer - Generate monitoring reports
• Tags: [ml, monitoring, drift, observability]

---

5. model-retraining-workflow

• Description: Automated model retraining triggered by drift or schedule
• Trigger: Drift alert or schedule
• Complexity: complex
• Duration: 1-4h
• QA Integration: validation: required, review: required
• Dependencies:
  • Agents: ml-engineer, data-scientist
  • Commands: /retrain-model, /compare-models
• Steps:
  1. Data refresh - data-scientist - Collect new training data
  2. Baseline comparison - ml-engineer - Document current performance
  3. Model retraining - ml-engineer - Train on updated data
  4. Champion/challenger - data-scientist - Compare with production
  5. Model promotion - ml-engineer - Promote if improved
• Tags: [ml, retraining, automation, drift]

---

Success Criteria

Criterion	Target	Measurement
Model Training Success	>= 95%	Successful training runs / Total runs
Model Performance	Meets baseline	Metric improvement over baseline
Deployment Success Rate	>= 99%	Successful deployments / Total deployments
Inference Latency	< 100ms P95	Model inference time
Data Drift Detection	< 24h	Time to detect significant drift
Model Retraining Time	< 4h	Time from trigger to deployment

---

Error Handling

Error Type	Recovery Strategy	Escalation
Training failure	Retry with checkpoints	Alert ML engineer
Resource exhaustion	Queue and scale resources	Alert DevOps
Data quality issues	Quarantine and alert	Alert data scientist
Deployment failure	Rollback to previous version	Alert ML engineer
Drift detected	Trigger retraining pipeline	Alert data scientist

---

Related Resources

• DATA-ENGINEERING-WORKFLOWS.md - Data pipelines
• ANALYTICS-BI-WORKFLOWS.md - Analytics H.P.006-WORKFLOWS
• WORKFLOW-DEFINITIONS-AI-ML-DATA.md - Extended ML H.P.006-WORKFLOWS

---

Maintainer: CODITECT Core Team Standard: CODITECT-STANDARD-WORKFLOWS v1.0.0