aj-geddes/useful-ai-prompts - Page 7

ml-model-explanation

Interpret machine learning models using SHAP, LIME, feature importance, partial dependence, and attention visualization for explainability

ml-model-training

Build and train machine learning models using scikit-learn, PyTorch, and TensorFlow for classification, regression, and clustering tasks

ml-pipeline-automation

Build end-to-end ML pipelines with automated data processing, training, validation, and deployment using Airflow, Kubeflow, and Jenkins

model-deployment

Deploy machine learning models to production using Flask, FastAPI, Docker, cloud platforms (AWS, GCP, Azure), and model serving frameworks

model-hyperparameter-tuning

Optimize hyperparameters using grid search, random search, Bayesian optimization, and automated ML frameworks like Optuna and Hyperopt

model-monitoring

Monitor model performance, detect data drift, concept drift, and anomalies in production using Prometheus, Grafana, and MLflow

natural-language-processing

Build NLP applications using transformers library, BERT, GPT, text classification, named entity recognition, and sentiment analysis

network-analysis

Analyze network structures, identify communities, measure centrality, and visualize relationships for social networks and organizational structures

neural-network-design

Design and architect neural networks with various architectures including CNNs, RNNs, Transformers, and attention mechanisms using PyTorch and TensorFlow

recommendation-engine

Build recommendation systems using collaborative filtering, content-based filtering, matrix factorization, and neural network approaches

recommendation-system

Build collaborative and content-based recommendation engines for product recommendations, personalization, and improving user engagement

regression-modeling

Build predictive models using linear regression, polynomial regression, and regularized regression for continuous prediction, trend forecasting, and relationship quantification

sentiment-analysis

Classify text sentiment using NLP techniques, lexicon-based analysis, and machine learning for opinion mining, brand monitoring, and customer feedback analysis

statistical-hypothesis-testing

Conduct statistical tests including t-tests, chi-square, ANOVA, and p-value analysis for statistical significance, hypothesis validation, and A/B testing

survival-analysis

Analyze time-to-event data, calculate survival probabilities, and compare groups using Kaplan-Meier and Cox proportional hazards models

time-series-analysis

Analyze temporal data patterns including trends, seasonality, autocorrelation, and forecasting for time series decomposition, trend analysis, and forecasting models

api-error-handling

Implement comprehensive API error handling with standardized error responses, logging, monitoring, retry logic, and validation patterns. Use when building resilient APIs, debugging issues, improving error reporting, implementing retry logic, handling HTTP…