Machine Learning Ops Ml Pipeline
Orchestrate a production-minded ML pipeline from data and features through training, registry, and serving when you are shipping models as a solo builder or tiny team.
Overview
Machine-learning-ops-ml-pipeline is an agent skill most often used in Build (also Ship, Operate) that designs and implements a full ML pipeline with multi-agent MLOps orchestration and production-oriented tooling.
Install
npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill machine-learning-ops-ml-pipelineWhat is this skill?
- Multi-agent, phase-based MLOps workflow with clear handoffs between specialized roles
- Integrates MLflow/W&B, Feast/Tecton-style features, and KServe/Seldon-style serving patterns
- Production-first defaults for scale, monitoring, reproducibility, and versioned data
- Parameterized design for your target problem via $ARGUMENTS in the skill invocation
- Points to resources/implementation-playbook.md when you need step-by-step build detail
- Phase-based multi-agent MLOps orchestration workflow
Adoption & trust: 463 installs on skills.sh; 40.1k GitHub stars; 3/3 security scanners passed (skills.sh audits).
What problem does it solve?
You know what model you want but lack a coordinated, reproducible path from experiments and features through deployment and ongoing reliability.
Who is it for?
Indie ML builders defining a new training-to-serving stack with MLflow/W&B-class tooling and clear agent handoffs between pipeline phases.
Skip if: One-off data plots, unrelated app CRUD, or teams that only need a single inference API with no training or feature pipeline.
When should I use this skill?
Working on machine learning pipeline - multi-agent MLOps orchestration tasks or workflows, or needing guidance, best practices, or checklists for that scope.
What do I get? / Deliverables
You leave with a structured, phase-based ML pipeline plan and implementation guidance aligned to experiment tracking, feature serving, and inference—ready to harden under Ship and Operate practices.
- ML pipeline architecture aligned to modern MLOps phases
- Actionable implementation steps with verification
- Reference path to implementation-playbook.md for detailed examples
Recommended Skills
Journey fit
Spans multiple journey phases - primary shelf plus alternate fits below.
Pipeline design and implementation are canonical Build work before anything is reliably monitored in production. End-to-end MLOps lands as backend systems—training jobs, feature stores, and inference services—not frontend or docs-only work.
Where it fits
Map training, feature store, and KServe/Seldon serving components before writing deployment code.
Define validation and smoke checks before promoting a model build to a staging endpoint.
Plan monitoring, versioning, and rollback hooks for the live inference path.
Bound MVP model scope and toolchain (W&B vs MLflow, Feast vs managed features) before committing build time.
How it compares
Use for end-to-end MLOps orchestration in chat, not as a drop-in hosted pipeline SaaS or a single-purpose model trainer skill.
Common Questions / FAQ
Who is machine-learning-ops-ml-pipeline for?
Solo and indie builders shipping ML-backed products who need a coordinated pipeline across experimentation, features, and serving—not just a notebook or a lone REST endpoint.
When should I use machine-learning-ops-ml-pipeline?
During Build when you are designing backend ML systems; during Ship when you are validating serving and monitoring hooks; and during Operate when you are hardening scale and reproducibility for production models.
Is machine-learning-ops-ml-pipeline safe to install?
Treat it as community-sourced procedural guidance; review the Security Audits panel on this Prism page and inspect any scripts or external services before running them in your environment.
SKILL.md
READMESKILL.md - Machine Learning Ops Ml Pipeline
# 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 <Task> subagent_type: data-engineer prompt: | Analyze and design data pipeline for ML system with requirements: $ARGUMENTS Deliverables: 1. 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 2. Data quality framework: - Profiling and statistics generation - Anomaly detection rules - Data lineage tracking - Quality gates and SLAs 3. Storage architecture: - Raw/processed/feature layers - Partitioning strategy - Retention policies - Cost optimization Provide implementation code for critical components and integration patterns. </Task> <Task> subagent_type: data-scientist prompt: | Design feature engineering and model requirements for: $ARGUMENTS Using data architecture from: {phase1.data-engineer.output} Deliverables: 1. Feature engineering pipeline: - Transformation specifications - Feature store schema (Feast/Tecton) - Statistical validation rules - Handling strategies for missing data/outliers 2. Model requirements: - Algorithm selection rationale - Performance metrics and baselines - Training data requirements - Evaluation criteria and thresholds 3. Experiment design: - Hypothesis and success metrics - A/B testing methodology - Sample size calculations - Bias detection approach Include feature transformation code and statistical validation logic. </Task> ## Phase 2: Model Development & Training <Task> subagent_type: ml-engineer prompt: | Implement training pipeline based on requirements: {phase1.data-scientist.output} Using data pipeline: {phase1.data-engineer.output} Build comprehensive training system: 1. Training pipeline implementation: - Modular training code with clear interfaces - Hyperparameter optimization (Optuna/Ray Tune) - Distributed training suppo