What a Lead } should know

Establishes algorithmic efficiency standards for ML systems in the team. Makes trade-off decisions between accuracy and speed at the architecture level. Evaluates ML pipeline scalability.

Shapes code quality culture in ML team. Introduces code review practices for ML. Standardizes approaches to ML component testing.

Defines data structure standards for ML team. Designs data abstractions for reuse across projects. Evaluates memory-efficiency of solutions.

Establishes OOP architectural standards for ML codebase. Conducts code review focusing on proper abstractions. Trains the team on ML framework design.

Defines ML API standards for the team. Designs unified API gateway for ML services. Coordinates ML API integration with backend teams.

Defines ML API design standards. Designs unified prediction API for all models. Coordinates ML API with frontend/backend teams.

Defines Docker standards for ML organization. Designs container strategy for ML platform. Coordinates with DevOps on ML-specific container requirements.

Defines Kubernetes strategy for ML platform. Designs multi-tenant ML infrastructure. Coordinates with Platform Engineering on ML requirements.

Defines testing standards for ML organization. Introduces ML-specific testing practices (data tests, model tests). Trains the team on ML testing.

Defines Spark strategy for ML data processing. Evaluates Spark vs alternatives (Dask, Ray) for ML workloads. Designs distributed computing architecture for ML.

Defines data quality strategy for ML organization. Introduces data quality culture in ML team. Coordinates with Data Engineering on data quality.

Defines data processing standards for ML team. Creates feature engineering framework. Trains the team on efficient data handling.

Defines ETL strategy for ML data. Coordinates with Data Engineering on ML data requirements. Designs data contracts for ML features.

Defines scikit-learn usage standards in the organization. Evaluates sklearn vs deep learning for different tasks. Creates feature engineering framework based on sklearn.

Defines experiment tracking standards. Introduces culture of experimentation. Standardizes metrics and evaluation.

Defines feature store strategy for the organization. Evaluates Feast vs Tecton vs custom solution. Designs feature governance and discovery.

Defines gradient boosting usage strategy in ML organization. Evaluates gradient boosting vs deep learning for tabular data. Creates AutoML pipeline.

Defines ML pipeline strategy. Standardizes pipeline components. Designs pipeline templating for faster development.

Defines experiment tracking strategy for the organization. Evaluates MLflow vs W&B vs ClearML. Designs model governance workflow. Standardizes ML lifecycle.

Defines model monitoring strategy. Standardizes monitoring practices. Designs model observability platform.

Defines model serving strategy for the platform. Designs unified serving layer. Optimizes serving costs. Coordinates with DevOps on infrastructure.

Defines deep learning strategy for the organization. Designs training infrastructure. Standardizes training patterns and evaluation. Coordinates GPU resources.

Defines code review practices for ML team. Conducts architecture review for ML projects. Trains the team on effective review.

Defines Git practices for ML organization. Designs repository strategy for ML. Standardizes branching model and review process.