ranbot-ai Machine Learning Pipeline - Multi-Agent MLOps Orchestration
Design and implement a complete ML pipeline for: $ARGUMENTS
Use this skill when
- Working on machine learning pipeline - multi-agent mlops orchestration tasks or workflows
- Needing guidance, best practices, or checklists for machine learning pipeline - multi-agent mlops orchestration
Do not use this skill when
- The task is unrelated to machine learning pipeline - multi-agent mlops orchestration
- 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.
- If detailed examples are required, open
resources/implementation-playbook.md.
Thinking
This workflow orchestrates multiple specialized agents to build a production-ready ML pipeline following modern MLOps best practices. The approach emphasizes:
- Phase-based coordination: Each phase builds upon previous outputs, with clear handoffs between agents
- Modern tooling integration: MLflow/W&B for experiments, Feast/Tecton for features, KServe/Seldon for serving
- Production-first mindset: Every component designed for scale, monitoring, and reliability
- Reproducibility: Version control for data, models, and infrastructure
- Continuous improvement: Automated retraining, A/B testing, and drift detection
The multi-agent approach ensures each aspect is handled by domain experts:
- Data engineers handle ingestion and quality
- Data scientists design features and experiments
- ML engineers implement training pipelines
- MLOps engineers handle production deployment
- Observability engineers ensure monitoring
Phase 1: Data & Requirements Analysis
Deliverables:
Data source audit and ingestion strategy:
- Source systems and connection patterns
- Schema validation using Pydantic/Great Expectations
- Data versioning with DVC or lakeFS
- Incremental loading and CDC strategies
Data quality framework:
- Profiling and statistics generation
- Anomaly detection rules
- Data lineage tracking
- Quality gates and SLAs
Storage architecture:
- Raw/processed/feature layers
- Partitioning strategy
- Retention policies
- Cost optimization
Provide implementation code for critical components and integration patterns.
Deliverables:
Feature engineering pipeline:
- Transformation specifications
- Feature store schema (Feast/Tecton)
- Statistical validation rules
- Handling strategies for missing data/outliers
Model requirements:
- Algorithm selection rationale
- Performance metrics and baselines
- Training data requirements
- Evaluation criteria and thresholds
Experiment design:
- Hypothesis and success metrics
- A/B testing methodology
- Sample size calculations
- Bias detection approach
Include feature transformation code and statistical validation logic.
Phase 2: Model Development & Training
Build comprehensive training system:
Training pipeline implementation:
- Modular training code with clear interfaces
- Hyperparameter optimization (Optuna/Ray Tune)
- Distributed training support (Horovod/PyTorch DDP)
- Cross-validation and ensemble strategies
Experiment tracking setup:
- MLflow/Weights & Biases integration
- Metric logging and visualization
- Artifact management (models, plots, data samples)
- Experiment comparison and analysis tools
Model registry integration:
- Version control and tagging strategy
- Model metadata and lineage
- Promotion workflows (dev -> staging -> prod)
- Rollback procedures
Provide complete training code with configuration management.
Focus areas:
Code quality and structure:
- Refactor for production standards
- Add comprehensive error handling
- Implement proper logging with structured formats
- Create reusable components and utilities
Performance optimization:
- Profile and optimize bottlenecks
- Implement caching strategies
- Optimize data loading and preprocessing
- Memory management for large-scale training
Testing framework:
- Unit tests for data transformations