MLOps and Observability

Experiment tracking and LLM observability guidance for W&B, MLflow, TensorBoard, LangSmith, and Phoenix.

Parent repository GitHub

Type: skill categories
Repository: Orchestra-Research/AI-Research-SKILLs
Readiness: Usable for guided research workflows, but framework guidance, autonomous claims, and demo results require project-specific validation
Keywords: 7

Location

Repository path

13-mlops/; 17-observability/

Invocation

Select MLOps or Observability in the interactive installer.

Setup

Installation / activation

Tracking, registry, visualization, tracing, evaluation, or monitoring tasks.

Keywords

MLOpsobservabilityW&BMLflowTensorBoardLangSmithPhoenix

Repository context

Orchestra-Research/AI-Research-SKILLs

MIT-licensed, cross-agent library of 98 stated SKILL.md research and engineering playbooks in 23 categories; for Claude Code it installs as individual or category skills and adds an autoresearch layer that routes literature, ideation, experimentation, analysis, artifact, and paper-writing work across domain skills.

AI researchMachine learning engineeringLarge language modelsModel trainingModel evaluationMechanistic interpretabilityInference and servingMLOps and infrastructureRetrieval-augmented generationMultimodal AIAI safety and alignmentScientific communication

Open full repository research