implementing-mlops
Strategic guidance for operationalizing machine learning models from experimentation to production. Covers experiment tracking (MLflow, Weights & Biases), model registry and versioning, feature stores (Feast, Tecton), model serving patterns (Seldon, KServe, BentoML), ML pipeline orchestration (Kubeflow, Airflow), and model monitoring (drift detection, observability). Use when designing ML infrastructure, selecting MLOps platforms, implementing continuous training pipelines, or establishing model governance.
npx skills add majiayu000/claude-skill-registry --skill implementing-mlops --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
The skill instructs the agent to provide strategic guidance for building production-grade ML infrastructure across the model lifecycle, including experiment tracking, model registry and versioning, feature stores, deployment patterns, pipeline orchestration, and monitoring. It emphasizes selecting platforms (MLflow, Weights & Biases, Neptune), feature stores (Feast, Tecton), serving tools (Seldon, KServe, BentoML, TorchServe, TensorFlow Serving), orchestration (Kubeflow, Airflow, Metaflow, Prefect, Dagster), deployment strategies, and monitoring tooling (Evidently AI, Prometheus+Grafana, Arize AI, Fiddler).
How it works
The skill provides structured guidance across sections:
- Experiment Tracking: outlines logs for parameters, metrics, artifacts, metadata, and compares MLflow, Weights & Biases, Neptune.ai with selection criteria.
- Model Registry and Versioning: defines artifacts, stage management (None, Staging, Production, Archived) and versioning schemes (Semantic Versioning and Git-based), plus provenance concepts.
- Feature Stores: explains online vs offline stores, platforms (Feast, Tecton, SageMaker Feature Store, Databricks Feature Store), and selection criteria.
- Model Serving Patterns: lists REST, gRPC, Batch, Streaming with tool options and latency characteristics; compares Seldon Core, KServe, BentoML, TorchServe, TensorFlow Serving, plus managed options.
- Deployment Strategies: covers Blue-Green, Canary, Shadow, A/B Testing, Multi-Armed Bandit with trade-offs.
- ML Pipeline Orchestration: describes training pipeline stages, continuous training pattern, and platforms (Kubeflow Pipelines, Apache Airflow, Metaflow, Prefect, Dagster).
- Model Monitoring and Observability: defines data drift, model drift, performance and business metrics, alerting thresholds, and tools (Evidently AI, Prometheus+Grafana, Arize AI, Fiddler).
- Model Optimization Techniques: details quantization, distillation, ONNX conversion, pruning, with expected impacts and tools.
- LLMOps Patterns: summarizes LoRA/QLoRA, prompt versioning, RAG monitoring, and LLM inference optimizations. The content is descriptive and prescriptive, listing concrete platforms, techniques, and evaluation criteria as the skill dictates.
When to use it
Use when designing ML infrastructure, selecting MLOps platforms, implementing continuous training pipelines, or establishing model governance.
What it can touch
Tools allowed: claude-code. Specific platforms and components are referenced, but the skill does not enumerate executable scripts beyond naming the tools and platforms.
Caveats
License: MIT. No explicit risk statements beyond general platform trade-offs and deployment considerations. No guarantees of outcome or performance improvements; focuses on guidance and comparisons.
# MLOps Patterns Operationalize machine learning models from experimentation to production deployment and monitoring. ## Purpose Provide strategic guidance for ML engineers and platform teams to build production-grade ML infrastructure. Cover the complete lifecycle: experiment tracking, model registry, feature stores, deployment patterns, pipeline orchestration, and monitoring. ## When to Use This Skill Use this skill when: - Designing MLOps infrastructure for production ML systems - Selecting experiment tracking platforms (MLflow, Weights & Biases, Neptune) - Implementing feature stores for online/offline feature serving - Choosing model serving solutions (Seldon Core, KServe, BentoML, TorchServe) - Building ML pipelines for training, evaluation, and deployment - Setting up model monitoring and drift detection - Establishing model governance and compliance frameworks - Optimizing ML inference costs and performance - Migrating from notebooks to production ML systems - Implementing continuous training and automated retraining ## Core Concepts ### 1. Experiment Tracking Track experiments systematically to ensure reproducibility and collaboration. **Key Components:** - Parameters: Hyp
- Purpose
- When to Use This Skill
- Core Concepts
- 1. Experiment Tracking
- 2. Model Registry and Versioning
- 3. Feature Stores
- 4. Model Serving Patterns
- 5. Deployment Strategies
- 6. ML Pipeline Orchestration
- 7. Model Monitoring and Observability
- 8. Model Optimization Techniques
- 9. LLMOps Patterns
- 10. Model Governance and Compliance
- Decision Frameworks
What does the implementing-mlops skill do?
Strategic guidance for operationalizing machine learning models from experimentation to production. Covers experiment tracking (MLflow, Weights & Biases), model registry and versioning, feature stores (Feast, Tecton), model serving patterns (Seldon, KServe, BentoML), ML pipeline orchestration (Kubeflow, Airflow), and model monitoring (drift detection, observability). Use when designing ML infrastructure, selecting MLOps platforms, implementing continuous training pipelines, or establishing model governance.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill implementing-mlops --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.