Ml Pipeline Workflow

Complete end-to-end MLOps pipeline orchestration from data preparation through model deployment.

Overview

This skill provides comprehensive guidance for building production ML pipelines that handle the full lifecycle: data ingestion → preparation → training → validation → deployment → monitoring.

Use this skill when

• -Building new ML pipelines from scratch
• -Designing workflow orchestration for ML systems
• -Implementing data → model → deployment automation
• -Setting up reproducible training workflows
• -Creating DAG-based ML orchestration
• -Integrating ML components into production systems

What This Skill Provides

Core Capabilities

1. Pipeline Architecture

• -End-to-end workflow design
• -DAG orchestration patterns (Airflow, Dagster, Kubeflow)
• -Component dependencies and data flow
• -Error handling and retry strategies

2. Data Preparation

• -Data validation and quality checks
• -Feature engineering pipelines
• -Data versioning and lineage
• -Train/validation/test splitting strategies

3. Model Training

• -Training job orchestration
• -Hyperparameter management
• -Experiment tracking integration
• -Distributed training patterns

4. Model Validation

• -Validation frameworks and metrics
• -A/B testing infrastructure
• -Performance regression detection
• -Model comparison workflows

5. Deployment Automation

• -Model serving patterns
• -Canary deployments
• -Blue-green deployment strategies
• -Rollback mechanisms

Reference Documentation

See the references/ directory for detailed guides:

• -data-preparation.md - Data cleaning, validation, and feature engineering
• -model-training.md - Training workflows and best practices
• -model-validation.md - Validation strategies and metrics
• -model-deployment.md - Deployment patterns and serving architectures

Assets and Templates

The assets/ directory contains:

• -pipeline-dag.yaml.template - DAG template for workflow orchestration
• -training-config.yaml - Training configuration template
• -validation-checklist.md - Pre-deployment validation checklist

Usage Patterns

Basic Pipeline Setup

# 1. Define pipeline stages
stages = [
    "data_ingestion",
    "data_validation",
    "feature_engineering",
    "model_training",
    "model_validation",
    "model_deployment"
]

# 2. Configure dependencies
# See assets/pipeline-dag.yaml.template for full example

Production Workflow

1. Data Preparation Phase

• -Ingest raw data from sources
• -Run data quality checks
• -Apply feature transformations
• -Version processed datasets

2. Training Phase

• -Load versioned training data
• -Execute training jobs
• -Track experiments and metrics
• -Save trained models

3. Validation Phase

• -Run validation test suite
• -Compare against baseline
• -Generate performance reports
• -Approve for deployment

4. Deployment Phase

• -Package model artifacts
• -Deploy to serving infrastructure
• -Configure monitoring
• -Validate production traffic

Best Practices

Pipeline Design

• -Modularity: Each stage should be independently testable
• -Idempotency: Re-running stages should be safe
• -Observability: Log metrics at every stage
• -Versioning: Track data, code, and model versions
• -Failure Handling: Implement retry logic and alerting

Data Management

• -Use data validation libraries (Great Expectations, TFX)
• -Version datasets with DVC or similar tools
• -Document feature engineering transformations
• -Maintain data lineage tracking

Model Operations

• -Separate training and serving infrastructure
• -Use model registries (MLflow, Weights & Biases)
• -Implement gradual rollouts for new models
• -Monitor model performance drift
• -Maintain rollback capabilities

Deployment Strategies

• -Start with shadow deployments
• -Use canary releases for validation
• -Implement A/B testing infrastructure
• -Set up automated rollback triggers
• -Monitor latency and throughput

Integration Points

Orchestration Tools

• -Apache Airflow: DAG-based workflow orchestration
• -Dagster: Asset-based pipeline orchestration
• -Kubeflow Pipelines: Kubernetes-native ML workflows
• -Prefect: Modern dataflow automation

Experiment Tracking

• -MLflow for experiment tracking and model registry
• -Weights & Biases for visualization and collaboration
• -TensorBoard for training metrics

Deployment Platforms

• -AWS SageMaker for managed ML infrastructure
• -Google Vertex AI for GCP deployments
• -Azure ML for Azure cloud
• -Kubernetes + KServe for cloud-agnostic serving

Progressive Disclosure

Start with the basics and gradually add complexity:

1. Level 1: Simple linear pipeline (data → train → deploy)

2. Level 2: Add validation and monitoring stages

3. Level 3: Implement hyperparameter tuning

4. Level 4: Add A/B testing and gradual rollouts

5. Level 5: Multi-model pipelines with ensemble strategies

Common Patterns

Batch Training Pipeline

# See assets/pipeline-dag.yaml.template
stages:
  - name: data_preparation
    dependencies: []
  - name: model_training
    dependencies: [data_preparation]
  - name: model_evaluation
    dependencies: [model_training]
  - name: model_deployment
    dependencies: [model_evaluation]

Real-time Feature Pipeline

# Stream processing for real-time features
# Combined with batch training
# See references/data-preparation.md

Continuous Training

# Automated retraining on schedule
# Triggered by data drift detection
# See references/model-training.md

Troubleshooting

Common Issues

• -Pipeline failures: Check dependencies and data availability
• -Training instability: Review hyperparameters and data quality
• -Deployment issues: Validate model artifacts and serving config
• -Performance degradation: Monitor data drift and model metrics

Debugging Steps

1. Check pipeline logs for each stage

2. Validate input/output data at boundaries

3. Test components in isolation

4. Review experiment tracking metrics

5. Inspect model artifacts and metadata

Next Steps

After setting up your pipeline:

1. Explore hyperparameter-tuning skill for optimization

2. Learn experiment-tracking-setup for MLflow/W&B

3. Review model-deployment-patterns for serving strategies

4. Implement monitoring with observability tools

Related Skills

• -experiment-tracking-setup: MLflow and Weights & Biases integration
• -hyperparameter-tuning: Automated hyperparameter optimization
• -model-deployment-patterns: Advanced deployment strategies