ml-azureml-adf-automationThis skill should be used when the user asks to automate Azure ML and Azure Data Factory production workflows. PROACTIVELY activate for: (1) Azure ML code asset registration, azure-ai-ml SDK, AML code versions, `result.version`, requested-vs-actual versions, (2) ADF to Azure ML orchestration, ADF We
ml-cloud-deploymentThis skill should be used when the user asks to deploy, scale, or cost-optimize ML workloads on cloud platforms. PROACTIVELY activate for: (1) AWS SageMaker Studio, Training, Processing, Pipelines, Endpoints, Model Monitor, Feature Store, Clarify, Ground Truth, EC2 GPUs, EKS, Lambda, Inferentia, Tra
ml-data-pipelineThis skill should be used when the user asks to ingest, clean, validate, transform, version, monitor, or serve ML data and features. PROACTIVELY activate for: (1) data ingestion, preprocessing, feature engineering, leakage prevention, train/serving skew, (2) Spark, Dask, Polars, pandas, Ray Data, st
ml-evaluationThis skill should be used when the user asks to evaluate, validate, compare, explain, or debug model performance. PROACTIVELY activate for: (1) metrics selection for classification, regression, ranking, NLP, CV, recommender, forecasting, and generative AI, (2) train/validation/test splits, cross-val
ml-fine-tuningThis skill should be used when the user asks to adapt pretrained, foundation, language, vision, multimodal, or embedding models. PROACTIVELY activate for: (1) transfer learning, full fine-tuning, frozen backbones, adapters, LoRA, QLoRA, AdaLoRA, PEFT, (2) Hugging Face Transformers, Diffusers, Accele
ml-hyperparameter-tuningThis skill should be used when the user asks to tune hyperparameters, run sweeps, optimize search spaces, or use AutoML. PROACTIVELY activate for: (1) Optuna, Ray Tune, FLAML, AutoGluon, Hyperopt, Nevergrad, KerasTuner, W&B sweeps, (2) grid search, random search, Bayesian optimization, TPE, Gaussian
ml-inference-optimizationThis skill should be used when the user asks to optimize, export, serve, compress, or accelerate ML inference. PROACTIVELY activate for: (1) latency, throughput, p95/p99, batching, concurrency, KV cache, memory, or cost issues, (2) quantization INT8/INT4, GPTQ, AWQ, bitsandbytes, pruning, sparsity,
ml-mlopsThis skill should be used when the user asks to productionize, track, version, govern, monitor, or automate ML systems. PROACTIVELY activate for: (1) MLflow, Weights & Biases, Neptune, Comet, ClearML experiment tracking, (2) model registry, model versioning, artifact lineage, reproducibility, (3) Ku
ml-trainingThis skill should be used when the user asks to train, debug, scale, or improve ML models. PROACTIVELY activate for: (1) PyTorch, TensorFlow/Keras, JAX, Flax, Hugging Face Trainer/Accelerate training loops, (2) distributed training, DDP/FSDP/DeepSpeed, TPU/GPU setup, (3) mixed precision AMP/bf16, gr