model-training

Data labeling: annotation guidelines, platforms, inter-annotator agreement, active learning, weak supervision, and label error detection.

1 skill · 1 command · 1 agent

ML data pipelines: Apache Beam, Spark, dbt, orchestration, streaming features, validation, and backfills.

1 skill · 1 command · 1 agent

Data versioning: DVC, Delta Lake, lineage, dataset cards, and reproducible dataset splits.

1 skill · 1 command · 1 agent

Distributed training: DDP, FSDP, DeepSpeed ZeRO, model parallelism, mixed precision, and multi-node debugging.

1 skill · 1 command · 1 agent

Experiment tracking: MLflow, Weights & Biases, Neptune, ClearML, hyperparameter search, and run organization.

1 skill · 1 command · 1 agent

Feature engineering: feature stores, transformations, selection, time-series features, leakage checks, and drift.

1 skill · 1 command · 1 agent

Query Trackio projects, runs, and metrics using the `trackio` CLI. Use when the user needs to list projects/runs/metrics, get summaries, or retrieve metric values from local Trackio databases. Supports both human-readable and JSON output formats. Covers project discovery, run inspection, metric querying, and system metrics. Designed for LLM agents and automation scripts.

1 skill

Full Domino Data Lab platform support — workspaces, jobs, model deployment, experiment tracking, GenAI tracing, Spark/Ray/Dask, and app deployment for data science teams

26 skills · 4 commands · 3 agents · 1 MCP server

Eval-gated LLM fine-tuning lifecycle (LoRA/QLoRA, DPO, GRPO/RLVR, vision SFT, quantized export). Use when fine-tuning an open-weights model end-to-end: building the eval harness first, selecting method and model, preparing data, training with Unsloth or TRL, gating checkpoints, and exporting GGUF/FP8.

10 skills · 2 commands · 3 agents

DataRobot skills for AI/ML workflows — model training, deployment, predictions, feature engineering, monitoring, explainability, data preparation, App Framework CI/CD, and external agent monitoring.

19 skills

Study companion for the Deep Learning textbook by Goodfellow, Bengio & Courville (MIT Press, 2016), free to read at deeplearningbook.org. Twenty chapter files, a glossary, patterns and a cheatsheet index the whole book, and a delta reference dates it against 2026 practice with per-claim confidence levels: double descent qualifying the U-curve, AdamW splitting weight decay from L2, transformers displacing Chapter 10's recurrence, diffusion growing out of Chapter 18's score matching. Four stdlib tools make it executable — a prerequisite-closed reading-path planner that refuses goals the book does not cover, a training diagnostic running Chapter 11's rules in priority order so a NaN is never reported as overfitting, a capacity planner that ranks 'shrink the model' last when overparameterized, and a parameter/FLOP/activation-memory calculator that refuses a stack whose shapes do not connect. A companion, not a compilation: the book is copyrighted, so nothing here reproduces its text.

1 skill · 3 commands · 1 agent

Data engineering, ML, and AI specialists - data pipelines, machine learning, LLM architecture