pricing
Guide for updating model pricing in Splitrail. Use when adding new AI model costs or updating existing pricing data.
发现 tools machine learning 相关 Skills,比较来源仓库和用途,查看适合你工作流的技能说明。
Guide for updating model pricing in Splitrail. Use when adding new AI model costs or updating existing pricing data.
Build declarative AI Services with LangChain4j using interface-based patterns, annotations, memory management, tools integration, and advanced application patterns. Use when implementing type-safe AI-powered fe...
Use to design composite intent scoring models with decay, weighting, and governance.
Use to drive consistent forecast methodology, grading, and inspection cadences.
Scoring system for opportunity hygiene, win likelihood, and inspection prioritization.
Use when designing, tuning, or auditing revenue forecast models.
Use to model economics, tiers, and impact forecasts for loyalty programs.
Design and architect neural networks with various architectures including CNNs, RNNs, Transformers, and attention mechanisms using PyTorch and TensorFlow
Build binary and multiclass classification models using logistic regression, decision trees, and ensemble methods for categorical prediction and classification
Create and transform features using encoding, scaling, polynomial features, and domain-specific transformations for improved model performance and interpretability
Interpret machine learning models using SHAP, LIME, feature importance, partial dependence, and attention visualization for explainability
Analyze temporal data patterns including trends, seasonality, autocorrelation, and forecasting for time series decomposition, trend analysis, and forecasting models
Implement computer vision tasks including image classification, object detection, segmentation, and pose estimation using PyTorch and TensorFlow
Build end-to-end ML pipelines with automated data processing, training, validation, and deployment using Airflow, Kubeflow, and Jenkins
Build recommendation systems using collaborative filtering, content-based filtering, matrix factorization, and neural network approaches
Identify unusual patterns, outliers, and anomalies in data using statistical methods, isolation forests, and autoencoders for fraud detection and quality monitoring
Reduce feature dimensionality using PCA, t-SNE, and feature selection for feature reduction, visualization, and computational efficiency
Build collaborative and content-based recommendation engines for product recommendations, personalization, and improving user engagement
Deploy machine learning models to production using Flask, FastAPI, Docker, cloud platforms (AWS, GCP, Azure), and model serving frameworks
Classify text sentiment using NLP techniques, lexicon-based analysis, and machine learning for opinion mining, brand monitoring, and customer feedback analysis
Build predictive models using linear regression, polynomial regression, and regularized regression for continuous prediction, trend forecasting, and relationship quantification
Determine cause-and-effect relationships using propensity scoring, instrumental variables, and causal graphs for policy evaluation and treatment effects
Monitor model performance, detect data drift, concept drift, and anomalies in production using Prometheus, Grafana, and MLflow
Optimize hyperparameters using grid search, random search, Bayesian optimization, and automated ML frameworks like Optuna and Hyperopt
默认按已收录数据中的 GitHub 仓库 Stars 排序。Stars 属于来源仓库,同一仓库的多个技能可能共享相同数值;它反映仓库关注度,不代表单个技能的安装量或质量评分。