Datasetpublic

stindardlogic/mlops-deployment-sft-100k

MLOps Deployment SFT 100K A synthetic supervised fine-tuning dataset of 100,000 high-quality conversations covering MLOps and ML model deployment — from model serving and inference optimization to monitoring, CI/CD, and production scaling. Designed to train AI assistants that can help ML engineers deploy and operate models at scale. Dataset Description This dataset covers the full MLOps lifecycle across 13 specialized categories. Each record follows the ShareGPT… See the full description on the dataset page: https://huggingface.co/datasets/stindardlogic/mlops-deployment-sft-100k.

sourceHugging Faceapache-2.0updated 3mo agoView on Hugging Face

2likes101downloads

Dataset Card

MLOps Deployment SFT 100K

A synthetic supervised fine-tuning dataset of 100,000 high-quality conversations covering MLOps and ML model deployment — from model serving and inference optimization to monitoring, CI/CD, and production scaling. Designed to train AI assistants that can help ML engineers deploy and operate models at scale.

Dataset Description

This dataset covers the full MLOps lifecycle across 13 specialized categories. Each record follows the ShareGPT format with a practitioner-level question and a detailed response with working code examples.

Categories

Category	Description
`model_serving`	FastAPI, BentoML, Triton, vLLM, multi-model routing
`mlflow_tracking`	Experiment tracking, model registry, artifact management
`deployment_patterns`	Blue-green, canary, shadow deployments, progressive rollout
`feature_stores`	Feast vs Tecton vs Hopsworks, online/offline features
`model_monitoring`	Data drift detection, Evidently, PSI, concept drift
`kubernetes_ml`	KServe, Seldon Core, auto-scaling for ML workloads
`cicd_ml`	GitHub Actions + DVC, model validation gates
`model_optimization`	Quantization (INT8, AWQ), pruning, ONNX export
`ab_testing_ml`	Traffic splitting, statistical significance for ML
`llm_inference`	vLLM, TGI, SGLang — high-throughput LLM serving
`cost_optimization`	GPU utilization, spot instances, request batching
`observability`	OpenTelemetry for ML, P99 monitoring, alerting

Format

ShareGPT format:

json

{
  "conversations": [
    {"from": "human", "value": "...MLOps question..."},
    {"from": "gpt", "value": "...production-ready response with code..."}
  ],
  "metadata": {"category": "...", "context": "..."},
  "id": "uuid"
}

Use Cases

—Fine-tuning AI assistants for ML deployment tasks
—Training models to reason about production ML operations
—Building AI-assisted MLOps tooling
—Educating teams on model serving and monitoring best practices

Quality Notes

All responses include working Python, YAML, and bash code using FastAPI, MLflow, vLLM, Kubernetes, Evidently, and other production MLOps tools.