What a Lead } should know

Defines Elasticsearch usage standards for the NLP team. Establishes best practices for text indexing, performance monitoring, and scaling search clusters.

Defines data processing standards for the NLP team. Establishes best practices for pandas/Polars usage, defines data processing patterns, and trains the team on optimization.

Defines scikit-learn usage standards for the NLP team. Establishes decision framework for choosing between ML and DL approaches, ensures model quality baseline.

Defines experiment tracking standards for the NLP team. Establishes reproducibility processes, metrics guidelines, and criteria for promoting models from experiment to production.

Defines LLM application strategy for the NLP team. Establishes architectural patterns, prompt engineering standards, and evaluation framework for LLM-based NLP systems.

Defines ML pipeline standards for the NLP team. Establishes MLOps best practices, defines model lifecycle management processes, and ensures reproducibility of all NLP experiments.

Defines MLflow usage standards for the NLP team. Establishes naming conventions, tagging strategy, model promotion workflow, and dashboard for monitoring NLP experiments.

Defines NLP model monitoring standards for the team. Establishes SLO/SLI for NLP services, incident response processes, and guidelines for interpreting drift signals.

Defines model serving strategy for the NLP team. Establishes deployment standards, SLA framework, and architectural decisions for scaling NLP inference infrastructure.

Defines NER strategy for the team. Establishes guidelines for annotation, model selection, evaluation methodology. Coordinates annotator work and ensures labeling consistency.

Defines PyTorch development standards for the NLP team. Establishes training best practices, model architecture guidelines, and ensures NLP experiment reproducibility.

Defines RAG strategy for the NLP team. Establishes evaluation framework, retrieval and generation best practices, and quality assurance standards for RAG-based NLP products.

Defines sentiment analysis strategy for the team. Establishes annotation standards, evaluation methodology, and architectural decisions for sentiment-based NLP products.

Defines text classification strategy for the team. Establishes taxonomy management processes, evaluation standards, and architectural decisions for classification-based NLP products.

Defines transformer strategy for the NLP team. Establishes model selection guidelines, fine-tuning approaches, and optimization techniques. Evaluates new architectures and their applicability.

Defines vector search strategy for the NLP team. Establishes embedding pipeline standards, index management, and evaluation metrics for semantic search systems.

Defines LLM API usage strategy for the NLP team. Establishes guidelines for choosing between ChatGPT, Claude, and open-source models for different NLP tasks. Manages API budget.

Defines prompt engineering standards for the NLP team. Establishes prompt library, prompt evaluation framework, and best practices for systematic prompt development.