exasol-distributed-mllisted

Distributed machine learning, data mining, and iterative HPC with Exasol. Covers end-to-end ML pipelines (DISTRIBUTE BY + SET scripts + BucketFS), per-entity federated training with partial_fit and ctx.reset(), batch inference, map-reduce ensemble training, distributed ensemble and SON algorithm for frequent itemset mining, Lua execute script orchestration for iterative algorithms (k-means, SGD, Apriori), parallel hyperparameter search, per-entity forecasting, anomaly detection, model lifecycle in BucketFS (pickle/joblib/ONNX versioning), GPU acceleration via CUDA SLCs (PyTorch/TensorFlow/RAPIDS), and ML-specific performance tuning (skew, OOM, multi-pass chunking).
exasol-labs/exasol-agent-skills · ★ 10 · AI & Automation · score 72

Install: claude install-skill exasol-labs/exasol-agent-skills

# Exasol Distributed ML and HPC Trigger when the user mentions: **distributed ML**, **machine learning**, **train model**, **batch inference**, **prediction**, **feature engineering**, **hyperparameter**, **PyTorch**, **TensorFlow**, **scikit-learn**, **RAPIDS**, **GPU model**, **model deployment**, **distributed training**, **ensemble**, **anomaly detection**, **forecasting**, **clustering at scale**, **k-means**, **gradient descent**, **iterative algorithm**, **frequent itemset**, **association rules**, **market basket**, **Apriori**, **FP-Growth**, **data mining**, **SON algorithm**, **partial_fit**, **federated training**, or any pattern where data is trained or scored inside Exasol. ## Routing Algorithm Choose the narrowest matching route. Load all routes that apply — they are designed to be read together. ### Route 1 — Pipeline architecture, algorithms, and patterns **Trigger phrases:** `distributed training`, `end-to-end ML`, `feature engineering`, `batch inference`, `ensemble`, `k-means`, `gradient descent`, `frequent itemset`, `association rules`, `market basket`, `Apriori`, `FP-Growth`, `data mining`, `federated training`, `per-entity model`, `anomaly detection`, `forecasting`, `hyperparameter search`, `map-reduce` → Load: **[references/distributed-ml-patterns.md](references/distributed-ml-patterns.md)** ### Route 2 — Model storage, versioning, and lifecycle **Trigger phrases:** `save model`, `ONNX`, `joblib`, `pickle`, `model versioning`, `load model in UDF