13 docs tagged with "ml"

Design and implement A/B test for model comparison including experimental design, traffic splitting, statistical power analysis, and significance testing.

Implement automated anomaly detection for metrics and KPIs using statistical methods, ML models, alerting, and root cause analysis.

Automated dataset preparation including data collection, cleaning, labeling, augmentation, and splitting for ML model training.

Systematic feature engineering including selection, transformation, encoding, scaling, and dimensionality reduction with feature importance analysis.

Self-validating agent that auto-categorizes financial transactions in CSV files

Forecast short-term and long-term electrical load for grid operations and planning.

Deploy trained ML model to production including containerization, API endpoint creation, load balancing, versioning, and rollback capability.

Comprehensive model evaluation using cross-validation, multiple metrics, confusion matrices, ROC/PR curves, and performance comparison against baselines.

Fine-tune pre-trained models on custom datasets including transfer learning, learning rate scheduling, early stopping, and model comparison.

Set up continuous monitoring for deployed models including data drift detection, concept drift detection, performance degradation alerts, and automated retraining triggers.

Complete supervised learning model training pipeline from data ingestion to model artifact storage with experiment tracking and hyperparameter optimization.

Systematic prompt engineering for LLMs including prompt design, few-shot examples, chain-of-thought prompting, evaluation, and version control.

Build end-to-end Retrieval-Augmented Generation pipeline including document ingestion, chunking, embedding, vector store, retrieval, and LLM integration.