AI Mlops

MLOps & ML Security — Complete Reference

Production ML lifecycle with modern security practices.

This skill covers:

• Production: Data ingestion, deployment, drift detection, monitoring, incident response
• Security: Prompt injection, jailbreak defense, RAG security, output filtering
• Governance: Privacy protection, supply chain security, safety evaluation

1. Data ingestion (dlt): Load data from APIs, databases to warehouses
2. Model deployment: Batch jobs, real-time APIs, hybrid systems, event-driven automation
3. Operations: Real-time monitoring, drift detection, automated retraining, incident response

Modern Best Practices (December 2025):

• Treat the model as a versioned production artifact with provenance, rollbacks, and audit logs (NIST SSDF: https://csrc.nist.gov/pubs/sp/800/218/final).
• Align governance and documentation to risk posture (EU AI Act: https://eur-lex.europa.eu/eli/reg/2024/1689/oj; NIST AI RMF: https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf).
• Measure and manage drift with clear triggers and playbooks; “drift” is not one metric.
• Make incident response and change management first-class (runbooks, on-call, postmortems).

It is execution-focused:

• Data ingestion patterns (REST APIs, database replication, incremental loading)
• Deployment patterns (batch, online, hybrid, streaming, event-driven)
• Automated monitoring with real-time drift detection
• Automated retraining pipelines (monitor → detect → trigger → validate → deploy)
• Incident handling with validated rollback and postmortems
• Links to copy-paste templates in templates/

Quick Reference

When to Use This Skill

Claude should invoke this skill when the user asks for deployment, operations, or data ingestion help, e.g.:

• "How do I deploy this model to prod?"
• "Design a batch + online scoring architecture."
• "Add monitoring and drift detection to our model."
• "Write an incident runbook for this ML service."
• "Package this LLM/RAG pipeline as an API."
• "Plan our retraining and promotion workflow."
• "Load data from Stripe API to Snowflake."
• "Set up incremental database replication with dlt."
• "Build an ELT pipeline for warehouse loading."

If the user is asking only about EDA, modelling, or theory, prefer:

• ai-ml-data-science (EDA, features, modelling, SQL transformation with SQLMesh)
• ai-llm (prompting, fine-tuning, eval)
• ai-rag (retrieval pipeline design)
• ai-llm-inference (compression, spec decode, serving internals)

If the user is asking about SQL transformation (after data is loaded), prefer:

• ai-ml-data-science (SQLMesh templates for staging, intermediate, marts layers)

Decision Tree: Choosing Deployment Strategy

User needs to deploy: [ML System]
    ├─ Data Ingestion?
    │   ├─ From REST APIs? → dlt REST API templates
    │   ├─ From databases? → dlt database sources (PostgreSQL, MySQL, MongoDB)
    │   └─ Incremental loading? → dlt incremental patterns (timestamp, ID-based)
    │
    ├─ Model Serving?
    │   ├─ Latency <500ms? → FastAPI real-time API
    │   ├─ Batch predictions? → Airflow/Dagster batch pipeline
    │   └─ Mix of both? → Hybrid (batch features + online scoring)
    │
    ├─ Monitoring & Ops?
    │   ├─ Drift detection? → Evidently + automated retraining triggers
    │   ├─ Performance tracking? → Prometheus + Grafana dashboards
    │   └─ Incident response? → Runbooks + PagerDuty alerts
    │
    └─ LLM/RAG Production?
        ├─ Cost optimization? → Caching, prompt templates, token budgets
        └─ Safety? → See ai-mlops skill

Core Concepts (Vendor-Agnostic)

• Lifecycle loop: train → validate → deploy → monitor → respond → retrain/retire.
• Risk controls: access control, data minimization, logging, and change management (NIST AI RMF: https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf).
• Observability planes: system metrics (latency/errors), data metrics (freshness/drift), quality metrics (model performance).
• Incident readiness: detection, containment, rollback, and root-cause analysis.

Do / Avoid

Do

• Do gate deployments with repeatable checks: evaluation pass, load test, security review, rollback plan.
• Do version everything: code, data, features, model artifact, prompt templates, configuration.
• Do define SLOs and budgets (latency/cost/error rate) before optimizing.

Avoid

• Avoid manual “clickops” deployments without audit trail.
• Avoid silent upgrades; require eval + canary for model/prompt changes.
• Avoid drift dashboards without actions; every alert needs an owner and runbook.

Core Patterns Overview

This skill provides 13 production-ready patterns organized into comprehensive guides:

Data & Infrastructure Patterns

Pattern 0: Data Contracts, Ingestion & Lineage → See Data Ingestion Patterns

• Data contracts with SLAs and versioning
• Ingestion modes (CDC, batch, streaming)
• Lineage tracking and schema evolution
• Replay and backfill procedures

Pattern 1: Choose Deployment Mode → See Deployment Patterns

• Decision table (batch, online, hybrid, streaming)
• When to use each mode
• Deployment mode selection checklist

Pattern 2: Standard Deployment Lifecycle → See Deployment Lifecycle

• Pre-deploy, deploy, observe, operate, evolve phases
• Environment promotion (dev → staging → prod)
• Gradual rollout strategies (canary, blue-green)

Pattern 3: Packaging & Model Registry → See Model Registry Patterns

• Model registry structure and metadata
• Packaging strategies (Docker, ONNX, MLflow)
• Promotion flows (experimental → production)
• Versioning and governance

Serving Patterns

Pattern 4: Batch Scoring Pipeline → See Deployment Patterns

• Orchestration with Airflow/Dagster
• Idempotent scoring jobs
• Validation and backfill procedures

Pattern 5: Real-Time API Scoring → See API Design Patterns

• Service design (HTTP/JSON, gRPC)
• Input/output schemas
• Rate limiting, timeouts, circuit breakers

Pattern 6: Hybrid & Feature Store Integration → See Feature Store Patterns

• Batch vs online features
• Feature store architecture
• Training-serving consistency
• Point-in-time correctness

Operations Patterns

Pattern 7: Monitoring & Alerting → See Monitoring Best Practices

• Data, performance, and technical metrics
• SLO definition and tracking
• Dashboard design and alerting strategies

Pattern 8: Drift Detection & Automated Retraining → See Drift Detection Guide

• Automated retraining triggers
• Event-driven retraining pipelines

Pattern 9: Incidents & Runbooks → See Incident Response Playbooks

• Common failure modes
• Detection, diagnosis, resolution
• Post-mortem procedures

Pattern 10: LLM / RAG in Production → See LLM & RAG Production Patterns

• Prompt and configuration management
• Safety and compliance (PII, jailbreaks)
• Cost optimization (token budgets, caching)
• Monitoring and fallbacks

Pattern 11: Cross-Region, Residency & Rollback → See Multi-Region Patterns

• Multi-region deployment architectures
• Data residency and tenant isolation
• Disaster recovery and failover
• Regional rollback procedures

Pattern 12: Online Evaluation & Feedback Loops → See Online Evaluation Patterns

• Feedback signal collection (implicit, explicit)
• Shadow and canary deployments
• A/B testing with statistical significance
• Human-in-the-loop labeling
• Automated retraining cadence

Resources (Detailed Guides)

For comprehensive operational guides, see:

Core Infrastructure:

• Data Ingestion Patterns - Data contracts, CDC, batch/streaming ingestion, lineage, schema evolution
• Deployment Lifecycle - Pre-deploy validation, environment promotion, gradual rollout, rollback
• Model Registry Patterns - Versioning, packaging, promotion workflows, governance
• Feature Store Patterns - Batch/online features, hybrid architectures, consistency, latency optimization

Serving & APIs:

• Deployment Patterns - Batch, online, hybrid, streaming deployment strategies and architectures
• API Design Patterns - ML/LLM/RAG API patterns, input/output schemas, reliability patterns, versioning

Operations & Reliability:

• Monitoring Best Practices - Metrics collection, alerting strategies, SLO definition, dashboard design
• Drift Detection Guide - Statistical tests, automated detection, retraining triggers, recovery strategies
• Incident Response Playbooks - Runbooks for common failure modes, diagnostics, resolution steps

Advanced Patterns:

• LLM & RAG Production Patterns - Prompt management, safety, cost optimization, caching, monitoring
• Multi-Region Patterns - Multi-region deployment, data residency, disaster recovery, rollback
• Online Evaluation Patterns - A/B testing, shadow deployments, feedback loops, automated retraining

Templates

Use these as copy-paste starting points for production artifacts:

Data Ingestion (dlt)

For loading data into warehouses and pipelines:

• dlt basic pipeline setup - Install, configure, run basic extraction and loading
• dlt REST API sources - Extract from REST APIs with pagination, authentication, rate limiting
• dlt database sources - Replicate from PostgreSQL, MySQL, MongoDB, SQL Server
• dlt incremental loading - Timestamp-based, ID-based, merge/upsert patterns, lookback windows
• dlt warehouse loading - Load to Snowflake, BigQuery, Redshift, Postgres, DuckDB

Use dlt when:

• Loading data from APIs (Stripe, HubSpot, Shopify, custom APIs)
• Replicating databases to warehouses
• Building ELT pipelines with incremental loading
• Managing data ingestion with Python

For SQL transformation (after ingestion), use:

→ ai-ml-data-science skill (SQLMesh templates for staging/intermediate/marts layers)

Deployment & Packaging

• Deployment & MLOps template - Complete MLOps lifecycle, model registry, promotion workflows
• Deployment readiness checklist - Go/No-Go gate, monitoring, and rollback plan
• API service template - Real-time REST/gRPC API with FastAPI, input validation, rate limiting
• Batch scoring pipeline template - Orchestrated batch inference with Airflow/Dagster, validation, backfill

Monitoring & Operations

• Monitoring & alerting template - Data/performance/technical metrics, dashboards, SLO definition
• Drift detection & retraining template - Automated drift detection, retraining triggers, promotion pipelines
• Incident runbook template - Failure mode playbooks, diagnosis steps, resolution procedures

Navigation

Resources

• resources/drift-detection-guide.md
• resources/model-registry-patterns.md
• resources/online-evaluation-patterns.md
• resources/monitoring-best-practices.md
• resources/llm-rag-production-patterns.md
• resources/api-design-patterns.md
• resources/incident-response-playbooks.md
• resources/deployment-patterns.md
• resources/data-ingestion-patterns.md
• resources/deployment-lifecycle.md
• resources/feature-store-patterns.md
• resources/multi-region-patterns.md

Templates

• template-dlt-pipeline.md
• template-dlt-rest-api.md
• template-dlt-database-source.md
• template-dlt-incremental.md
• template-dlt-warehouse-loading.md
• templates/deployment/template-deployment-mlops.md
• templates/deployment/deployment-readiness-checklist.md
• templates/deployment/template-api-service.md
• templates/deployment/template-batch-pipeline.md
• templates/ops/template-incident-runbook.md
• templates/monitoring/template-drift-retraining.md
• templates/monitoring/template-monitoring-plan.md

Data

• data/sources.json — Curated external references

External Resources

See data/sources.json for curated references on:

• Serving frameworks (FastAPI, Flask, gRPC, TorchServe, KServe, Ray Serve)
• Orchestration (Airflow, Dagster, Prefect)
• Model registries and MLOps (MLflow, W&B, Vertex AI, Sagemaker)
• Monitoring and observability (Prometheus, Grafana, OpenTelemetry, Evidently)
• Feature stores (Feast, Tecton, Vertex, Databricks)
• Streaming & messaging (Kafka, Pulsar, Kinesis)
• LLMOps & RAG infra (vector DBs, LLM gateways, safety tools)

Data Lake & Lakehouse

For comprehensive data lake/lakehouse patterns (beyond dlt ingestion), see data-lake-platform:

• Table formats: Apache Iceberg, Delta Lake, Apache Hudi
• Query engines: ClickHouse, DuckDB, Apache Doris, StarRocks
• Alternative ingestion: Airbyte (GUI-based connectors)
• Transformation: dbt (alternative to SQLMesh)
• Streaming: Apache Kafka patterns
• Orchestration: Dagster, Airflow

This skill focuses on ML-specific deployment, monitoring, and security. Use data-lake-platform for general-purpose data infrastructure.

Related Skills

For adjacent topics, reference these skills:

• ai-ml-data-science - EDA, feature engineering, modelling, evaluation, SQLMesh transformations
• ai-llm - Prompting, fine-tuning, evaluation for LLMs
• ai-agents - Agentic workflows, multi-agent systems, LLMOps
• ai-rag - RAG pipeline design, chunking, retrieval, evaluation
• ai-llm-inference - Model serving optimization, quantization, batching
• ai-prompt-engineering - Prompt design patterns and best practices
• data-lake-platform - Data lake/lakehouse infrastructure (ClickHouse, Iceberg, Kafka)

Use this skill to turn trained models into reliable services, not to derive the model itself.