Inicio Rápido de Machine Learning con Databricks

Resumen

85% of ML models never reach production—but yours will. This Short Course was created to help Machine Learning and Artificial Intelligence professionals accomplish rapid ML deployment using Databricks enterprise workflows.

By completing this course, you'll be able to track experiments with MLflow, leverage AutoML to accelerate model development, and deploy serving endpoints with production-grade performance monitoring—skills you can apply immediately to your data pipelines. By the end of this course, you will be able to:

● Apply MLflow tracking to log runs, metrics, and artifacts for a baseline and AutoML-generated model within a Databricks workspace (Apply) ● Analyze AutoML experiment results to select a candidate model based on accuracy, runtime, and feature importance reports (Analyze) ● Evaluate model-serving endpoint performance and access controls to confirm readiness for production deployment (Evaluate) This course is unique because it provides hands-on experience with Databricks' unified platform, combining experiment tracking, automated machine learning, and model serving in a single integrated workflow that mirrors real enterprise deployment patterns.

Programa

• MÓDULO 1: Seguimiento de MLflow y Registro de Experimentos
Descripción a Alto Nivel: Establecer un seguimiento sistemático de experimentos utilizando MLflow para crear flujos de trabajo de ML auditables para el cumplimiento normativo.
• MÓDULO 2: Análisis AutoML y Selección de Modelos
Descripción a Alto Nivel: Aprovechar las capacidades de AutoML para acelerar el desarrollo de modelos manteniendo estándares rigurosos de evaluación para la evaluación de riesgo crediticio.
• MÓDULO 3: Despliegue de Modelos y Evaluación de Puntos de Acceso
Descripción a Alto Nivel: Completar el flujo de trabajo de ML desplegando puntos de acceso para servicios listos para producción, con validación de rendimiento integral y controles de acceso.

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Impartido por

Hurix Digital

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Materias

Data Science