Descripción
Este recurso está en inglés
Development environments might not have the exact requirements as production environments. Moving data science and machine learning projects from idea to production requires state-of-the-art skills. You need to architect and implement your projects for scale and operational efficiency. Data science is an interdisciplinary field that combines domain knowledge with mathematics, statistics, data visualization, and programming skills.
The Practical Data Science Specialization brings together these disciplines using purpose-built ML tools in the AWS cloud. It helps you develop the practical skills to effectively deploy your data science projects and overcome challenges at each step of the ML workflow using Amazon SageMaker.
Rutas de aprendizaje gratuitas por trayectoria profesional
Data ScientistCursos IncluidosCourseraDeepLearning.AIFree
1. Analyze Datasets and Train ML Models using AutoML
19 horas 
Avanzado
15 075CourseraDeepLearning.AIFree
2. Build, Train, and Deploy ML Pipelines using BERT
14 horas
Avanzado
6 743CourseraDeepLearning.AI
3. Optimize ML Models and Deploy Human-in-the-Loop Pipelines
14 horas
Avanzado
3 753
19 horas
Avanzado
15 075
14 horas
Avanzado
6 743
14 horas
Avanzado
3 753