Skill UI

Automates the end-to-end training of machine learning models by analyzing datasets, selecting suitable algorithms, tuning parameters, performing cross-validated training, and emitting performance metrics plus a saved model artifact.

Weights & Biases streamlines MLOps by logging metrics, configs, and artifacts, offering dashboards to monitor training, compare runs, optimize sweeps, and share models with teams.

PyHealth is a Python healthcare AI toolkit for building, training, evaluating, and deploying clinical prediction models using EHR, signal, imaging, and coding data while optimizing pipelines, calibration, and interpretability.

PyTorch Lightning organizes models into LightningModule, data modules, callbacks, logging, and Trainer orchestration for scalable multi-GPU/TPU training with distributed strategies and experiment tracking.

Stable Baselines3 supplies PyTorch-based RL algorithms with a scikit-learn-style API for training agents, crafting Gymnasium environments, running vectorized workloads, wiring callbacks, and persisting models—ideal for single-agent experimentation and rapid RL prototyping.

Load Hugging Face Transformers to run pipelines for text, vision, audio, and multimodal inference, customize model loading, tokenize inputs, and fine-tune with distributed training on domain-specific datasets.

Train or fine-tune language models with TRL on Hugging Face Jobs, covering SFT, DPO, GRPO, and reward modeling plus GGUF export, while submitting scripts via hf_jobs and tracking via Trackio on managed cloud GPUs.