技能档案

ML Model Evaluation

此技能定义了各角色和级别的期望。

Machine Learning & AI MLOps

角色数

包含此技能的角色

级别数

结构化成长路径

必要要求

其余 10 个可选

Machine Learning & AI

MLOps

2026/2/22

选择当前级别并对比期望。下方卡片显示晋升所需掌握的内容。

各级别期望

表格展示从初级到首席的技能深度变化。点击行查看详情。

角色	必要性	描述
Data Scientist		Understands core ML metrics: accuracy, precision, recall, F1, ROC-AUC for classification; RMSE, MAE, R² for regression. Conducts cross-validation for generalization assessment. Builds confusion matrix and classification report via scikit-learn.
LLM Engineer		Knows basic ML metrics: accuracy, precision, recall, F1. Computes metrics for classification and regression models used in LLM system preprocessing pipelines.

角色	必要性	描述
Data Scientist		Applies advanced evaluation methods: stratified cross-validation, time-series split, nested cross-validation. Evaluates models considering business metrics: lift, gain charts, expected calibration error. Analyzes models for fairness and bias via disaggregated metrics.
LLM Engineer		Independently conducts comprehensive ML model evaluation: confusion matrix, ROC-AUC, calibration plots. Evaluates auxiliary ML models in LLM pipelines: safety classifiers, intent detectors.

角色	必要性	描述
Data Scientist		Designs comprehensive evaluation frameworks for ML models: offline metrics, online metrics, business KPIs. Implements automated model validation gates before production deployment. Applies counterfactual analysis and SHAP for deep model diagnostics.
LLM Engineer		Designs evaluation frameworks for ML components of the LLM ecosystem: cross-validation strategies, statistical significance testing, fairness metrics. Automates regression testing.

角色	必要性	描述
Data Scientist		Defines model evaluation standards for the data science team. Establishes evaluation checklist for each ML task type. Coordinates model review process and alignment between offline metrics and business outcomes.
LLM Engineer		Defines ML evaluation standards for the LLM team. Establishes guidelines for auxiliary ML model assessment, threshold selection, and A/B testing methodology.

角色	必要性	描述
Data Scientist		Shapes ML quality assurance strategy at organizational level. Defines enterprise model validation standards and audit requirements. Publishes evaluation methodologies for industry and shapes thought leadership.
LLM Engineer		Shapes enterprise ML evaluation strategy. Defines approaches to model quality governance, automated evaluation pipelines, and alignment of ML metrics with business KPIs at organizational scale.

Junior 2 要求

Data Scientist

Understands core ML metrics: accuracy, precision, recall, F1, ROC-AUC for classification; RMSE, MAE, R² for regression. Conducts cross-validation for generalization assessment. Builds confusion matrix and classification report via scikit-learn.
LLM Engineer

Knows basic ML metrics: accuracy, precision, recall, F1. Computes metrics for classification and regression models used in LLM system preprocessing pipelines.

Middle 2 要求

Data Scientist

Applies advanced evaluation methods: stratified cross-validation, time-series split, nested cross-validation. Evaluates models considering business metrics: lift, gain charts, expected calibration error. Analyzes models for fairness and bias via disaggregated metrics.
LLM Engineer

Independently conducts comprehensive ML model evaluation: confusion matrix, ROC-AUC, calibration plots. Evaluates auxiliary ML models in LLM pipelines: safety classifiers, intent detectors.

Senior 2 要求

Data Scientist

Designs comprehensive evaluation frameworks for ML models: offline metrics, online metrics, business KPIs. Implements automated model validation gates before production deployment. Applies counterfactual analysis and SHAP for deep model diagnostics.
LLM Engineer

Designs evaluation frameworks for ML components of the LLM ecosystem: cross-validation strategies, statistical significance testing, fairness metrics. Automates regression testing.

Lead / Staff 2 要求

Data Scientist

Defines model evaluation standards for the data science team. Establishes evaluation checklist for each ML task type. Coordinates model review process and alignment between offline metrics and business outcomes.
LLM Engineer

Defines ML evaluation standards for the LLM team. Establishes guidelines for auxiliary ML model assessment, threshold selection, and A/B testing methodology.

Principal 2 要求

Data Scientist

Shapes ML quality assurance strategy at organizational level. Defines enterprise model validation standards and audit requirements. Publishes evaluation methodologies for industry and shapes thought leadership.
LLM Engineer

Shapes enterprise ML evaluation strategy. Defines approaches to model quality governance, automated evaluation pipelines, and alignment of ML metrics with business KPIs at organizational scale.

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