MLOps CI/CD Platform

User Stories

As an ML engineer, I want automated model retraining triggered by data updates so I can maintain model freshness without manual intervention
As a data scientist, I want experiment tracking integrated into the pipeline so I can compare model versions and reproduce results
As a DevOps engineer, I want shadow deployments for new models so I can validate performance before affecting production
As a platform architect, I want quality gates at each pipeline stage so I can ensure only validated models reach production
As a compliance officer, I want full audit trails of model deployments so I can demonstrate regulatory compliance

Industry Applications

Financial Services

Automated fraud model updates, credit scoring pipelines, risk model validation

Healthcare

Clinical model deployment, diagnostic AI updates, patient outcome prediction

E-commerce

Recommendation engine updates, demand forecasting, dynamic pricing models

Manufacturing

Predictive maintenance models, quality control AI, supply chain optimization

Technology

Search ranking models, content moderation, personalization engines

Implementation Steps

Step 01

Data Validation

Automated schema validation, anomaly detection, and data quality checks on incoming training data

Step 02

Feature Engineering

Versioned feature pipelines with feature store integration for consistent feature computation

Step 03

Model Training

Distributed training with experiment tracking, hyperparameter optimization, and artifact versioning

Step 04

Model Evaluation

Automated benchmark testing, bias detection, and performance validation against baselines

Step 05

Staging Deployment

Shadow deployment with A/B testing, canary releases, and production traffic simulation

Step 06

Production Deployment

Blue-green deployment with automated rollback, monitoring, and alerting integration

Core Components

Component	Function	Tools
Pipeline Orchestration	Workflow scheduling, dependency management, DAG execution	Kubeflow, Argo CD, Apache Airflow
Version Control	Code versioning, data versioning, model lineage tracking	Git, DVC, MLflow
Experiment Tracking	Parameter logging, metric visualization, artifact management	MLflow, Weights & Biases, Neptune.ai
Model Registry	Model versioning, stage transitions, deployment metadata	MLflow Registry, Vertex AI Model Registry
CI/CD Engine	Automated builds, testing, deployment triggers	Jenkins, GitLab CI/CD, GitHub Actions
Monitoring	Model performance, data drift, system health	Evidently AI, WhyLabs, Prometheus