Browse Skills

Design intelligent model routing systems that optimize cost, latency, and quality across multiple LLM providers. Covers cascading strategies, confidence-based escalation, prompt caching economics, and fallback architectures.

Systematic optimization of machine learning model hyperparameters using Bayesian optimization, multi-fidelity methods, and distributed search strategies to find optimal configurations efficiently.

Design and optimize manufacturing facility layouts using digital twin simulation. Create virtual replicas of production floors to test layout alternatives, identify bottlenecks, and validate material flow improvements before physical implementation.

Design and implement retrieval-augmented generation systems for legal documents with citation-safe retrieval, hallucination prevention, and privilege-aware document handling.

Expert in unsupervised learning techniques — clustering, dimensionality reduction, anomaly detection, and pattern discovery with scikit-learn, UMAP, and HDBSCAN.

Optimizes few-shot prompting strategies for large language models by selecting, ordering, and formatting demonstration examples that maximize output quality while minimizing token usage and cost.

Ensure offline/online feature consistency in ML systems by designing validation pipelines, detecting training-serving skew, and implementing point-in-time correctness guarantees across feature store architectures.

Expert guide for deploying TensorFlow models to production using TensorFlow Serving, covering SavedModel optimization, serving infrastructure, batching strategies, model versioning, and monitoring for reliable ML inference at scale.

Designs end-to-end computer vision pipelines including model selection (CNN/ViT/YOLO), data augmentation, training strategies, transfer learning, and production deployment with ONNX/TensorRT optimization for cloud and edge.

Design and operate low-latency feature delivery systems for production ML, including feature store architecture, online/offline consistency, streaming pipelines, and serving infrastructure optimization.

Design and implement structured deep learning training workflows using PyTorch Lightning, covering LightningModule architecture, distributed training strategies, experiment tracking, checkpoint management, and production model deployment.

Guides ML practitioners in designing rigorous experiment tracking workflows using MLflow, Weights & Biases, and related tools, covering hyperparameter optimization, reproducibility, model registry, and team collaboration patterns.

Evaluates machine learning models with rigorous methodology and statistical analysis. Covers metrics selection, cross-validation strategies, bias detection, A/B testing, confusion matrices, ROC/AUC analysis, and comprehensive evaluation reporting.

Designs effective prompts and prompt chains for LLM applications. Covers chain-of-thought reasoning, few-shot learning, system prompt architecture, output formatting, prompt evaluation, and reusable template design.