# #ml — MDRSS hashtag feed

> Public MDRSS cards tagged #ml.
> Canonical feed: https://mdrss.com/feeds/ml

## Cards (9)

### [Machine Learning Pipeline - Multi-Agent MLOps Orchestration](https://mdrss.com/llm-engineering/mlops-and-ml-systems/901383/901383.md)

This workflow orchestrates multiple specialized agents to build a production-ready ML pipeline following modern MLOps best practices. The approach emphasizes:. Use it to give an agent explicit responsibilities, steps and constraints.

Classification: llm-engineering/mlops-and-ml-systems · Feed: llm-engineering · Updated: 2026-08-04T13:54:51.641Z · Version: 1

### [Mlops engineer](https://mdrss.com/llm-engineering/mlops-and-ml-systems/901382/901382.md)

You are an MLOps engineer specializing in ML infrastructure, automation, and production ML systems across cloud platforms. Use it to give an agent explicit responsibilities, steps and constraints.

Classification: llm-engineering/mlops-and-ml-systems · Feed: llm-engineering · Updated: 2026-08-04T13:54:51.641Z · Version: 1

### [Ml engineer](https://mdrss.com/llm-engineering/mlops-and-ml-systems/901381/901381.md)

You are an ML engineer specializing in production machine learning systems, model serving, and ML infrastructure. Use it to give an agent explicit responsibilities, steps and constraints.

Classification: llm-engineering/mlops-and-ml-systems · Feed: llm-engineering · Updated: 2026-08-04T13:54:51.641Z · Version: 1

### [Data scientist](https://mdrss.com/llm-engineering/mlops-and-ml-systems/901380/901380.md)

You are a data scientist specializing in advanced analytics, machine learning, statistical modeling, and data-driven business insights. Use it to give an agent explicit responsibilities, steps and constraints.

Classification: llm-engineering/mlops-and-ml-systems · Feed: llm-engineering · Updated: 2026-08-04T13:54:51.641Z · Version: 1

### [Preference Optimization](https://mdrss.com/llm-engineering/training-and-fine-tuning/901373/901373.md)

This skill assumes finetuning-method-selection already routed here because the data shape is preference pairs or unpaired thumbs-up/down feedback, not demonstrations (that's lora-qlora-recipes) or a verifiable reward signal (that's grpo-rlvr-training). What follows is metho. Use it to give an agent explicit responsibilities, steps and constraints.

Classification: llm-engineering/training-and-fine-tuning · Feed: llm-engineering · Updated: 2026-08-04T13:54:51.641Z · Version: 1

### [GRPO & RLVR Training](https://mdrss.com/llm-engineering/training-and-fine-tuning/901368/901368.md)

This skill assumes finetuning-method-selection already routed here because the target behavior has a verifiable pass/fail signal — not demonstrations (lora-qlora-recipes) or preference pairs (preference-optimization). What follows is when RL is the right tool, the reference. Use it to give an agent explicit responsibilities, steps and constraints.

Classification: llm-engineering/training-and-fine-tuning · Feed: llm-engineering · Updated: 2026-08-04T13:54:51.641Z · Version: 1

### [Llm finetuning architect](https://mdrss.com/llm-engineering/training-and-fine-tuning/901354/901354.md)

You are the fine-tuning architect: a skeptical strategist who decides whether fine-tuning is the right tool at all before anyone opens a training config. You are the gate-keeper standing between "the user wants to fine-tune" and the first line of a training script — most requests. Use it to give an agent explicit responsibilities, steps and constraints.

Classification: llm-engineering/training-and-fine-tuning · Feed: llm-engineering · Updated: 2026-08-04T13:54:51.641Z · Version: 1

### [What is NannyML?](https://mdrss.com/llm-engineering/models-and-training/1663/1663.md)

Website • Docs • Community Slack NannyML is an open-source python library that allows you to estimate post-deployment model performance (without access to targets), detect data drift, and intelligently link data drift alerts back to changes in model performance. Built for data scientists, NannyML has an easy-to-use interface, interactive visualizations, is completely model-agnostic and currently supports all tabular use cases, classification and regression.

Classification: llm-engineering/models-and-training · Feed: llm-engineering · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [MLX-LM-LORA](https://mdrss.com/llm-engineering/models-and-training/1483/1483.md)

With MLX-LM-LoRA you can, train Large Language Models locally on Apple Silicon using MLX. Training works with all models supported by MLX-LM, including: Training Types: Training Algorithms: Quantization Aware Training (QAT): Training Your Custom Preference Model: --- The main command is mlxlmlora.train.

Classification: llm-engineering/models-and-training · Feed: llm-engineering · Updated: 2026-08-04T12:22:38.168Z · Version: 1