Setup Automl Pipeline
Skill Verifiziert AktivConfigure automated machine learning pipelines using Optuna or Ray Tune for hyperparameter optimization. Implement efficient search strategies (Hyperband, ASHA), define search spaces, and set up early stopping to find optimal model configurations with minimal manual tuning. Use when starting a new ML project and needing to quickly find good configurations, retraining with new data and re-optimizing hyperparameters, comparing multiple algorithms, or when the team lacks deep expertise in specific algorithm hyperparameters.
Automate the process of finding optimal model configurations for machine learning projects, saving time and expertise.
Funktionen
- Configure automated ML pipelines
- Hyperparameter optimization with Optuna/Ray Tune
- Implement search strategies (Hyperband, ASHA)
- Set up early stopping for trials
- Track experiments with MLflow
- Save and deploy optimized models
Anwendungsfälle
- Starting new ML projects and finding good configurations
- Retraining models with new data and re-optimizing hyperparameters
- Comparing multiple algorithms and their optimal settings
- Teams lacking deep expertise in specific algorithm hyperparameters
Nicht-Ziele
- Feature engineering
- Building custom ML algorithms
- Production deployment orchestration (e.g., Kubernetes, CI/CD)
- Real-time inference serving infrastructure
Documentation
- info:Configuration & parameter referenceWhile the SKILL.md outlines steps and provides code snippets, a detailed reference for all configuration options, defaults, and precedence orders for environment variables used within the scripts is not explicitly documented.
Code Execution
- info:ValidationWhile installation and setup steps are provided, explicit schema validation for input arguments or structured output within the provided scripts is not detailed.
- info:Error HandlingThe SKILL.md outlines expected failures and recovery steps for installation, but the provided code snippets do not explicitly show robust error handling for all internal logic paths.
Errors
- info:Actionable error messagesThe SKILL.md lists common pitfalls and failure modes with some recovery steps, but the code snippets themselves could benefit from more explicit error handling and user-facing messages.
Installation
/plugin install agent-almanac@pjt222-agent-almanacQualitätspunktzahl
VerifiziertVertrauenssignale
Ähnliche Erweiterungen
TimesFM Forecasting
100Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use.
SHAP Model Interpretability
100Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.
PyTorch Lightning
100Deep learning framework (PyTorch Lightning). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.
Embedding Strategies
100Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.
Aws Cdk Development
100AWS Cloud Development Kit (CDK) Experte für den Aufbau von Cloud-Infrastruktur mit TypeScript/Python. Verwenden Sie dies beim Erstellen von CDK-Stacks, Definieren von CDK-Konstrukten, Implementieren von Infrastructure as Code oder wenn der Benutzer CDK, CloudFormation, IaC, cdk synth, cdk deploy erwähnt oder AWS-Infrastruktur programmatisch definieren möchte. Behandelt CDK-App-Struktur, Konstruktmuster, Stack-Komposition und Bereitstellungs-Workflows.
Fit Drift Diffusion Model
100Fit cognitive drift-diffusion models (Ratcliff DDM) to reaction time and accuracy data with parameter estimation (drift rate, boundary separation, non-decision time), model comparison, and parameter recovery validation. Use when modeling binary decision-making with reaction time data, estimating cognitive parameters from experimental data, comparing sequential sampling model variants, or decomposing speed-accuracy tradeoff effects into latent cognitive components.