sickn33/antigravity-awesome-skills/ml-pipeline-workflow

Supporting material availability

This catalog copy contains the inline guidance below, but does not include the following supporting paths. They are external prerequisites, not bundled files:

• <support-root>/assets/pipeline-dag.yaml.template
• <support-root>/references/data-preparation.md
• <support-root>/references/model-training.md

Ask for the original supporting material or an existing installation before using a step that depends on these paths; this catalog does not identify a verified download source for them. Resolve each path relative to that installation, inspect its contents, and verify any applicable license and execution prerequisites. Do not run a command or claim a referenced procedure was followed until its required file exists. Do not fabricate the missing scripts or treat an unrelated file with the same name as equivalent. Independent steps fully documented here can proceed within the user’s authorized scope; report any dependent step as unavailable.

ML Pipeline Workflow

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

Do not use this skill when

• The task is unrelated to ml pipeline workflow
• You need a different domain or tool outside this scope

Instructions

• Clarify goals, constraints, and required inputs.
• Apply relevant best practices and validate outcomes.
• Provide actionable steps and verification.

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

1. Data Preparation

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

1. Model Training

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

1. Model Validation

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

1. Deployment Automation

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

Reference Documentation

The following upstream reference guides are not bundled in this catalog tree; obtain and inspect them before relying on their procedures:

• 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 following upstream assets are not bundled in this catalog tree:

• 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

1. Training Phase

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

1. Validation Phase

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

1. Deployment Phase

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

Evaluation boundaries

Split by the actual independence unit (for example entity/time), fit preprocessing only on training data, and keep the final test set out of tuning. Pin datasets/code/model artifacts and record baseline metrics plus uncertainty. Cloud training, external experiment tracking and deployment require data-sharing/cost authorization. Treat the inline YAML as a conceptual DAG, not a directly executable Airflow/Kubeflow schema.

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

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.