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.

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.

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.

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.

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.

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.

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.

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.

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.