Curso ML Pipelines on Google Cloud

• Científicos de datos que busquen generar un impacto comercial mediante la conversión rápida del prototipo de machine learning a la producción.
• Ingenieros de software que busquen desarrollar habilidades de ingeniería de machine learning.
• Ingenieros de machine learning que deseen adoptar Google Cloud.

• Organizar el entrenamiento y la implementación de modelos con TFX y Cloud AI Platform.
• Operar modelos de machine learning implementados de manera efectiva y eficiente.
• Realizar una capacitación continua con varios marcos (Scikit Learn, XGBoost, PyTorch) y organizar pipelines con Cloud Composer y MLFlow.
• Integre los flujos de trabajo de machine learning con los flujos de trabajo de gestión de datos ascendentes y descendentes para mantener la gestión integral de linaje y metadatos.

Módulo 1: Introduction to TFX

• Develop a high level understanding of TFX standard pipeline components.
• Learn how to use a TFX Interactive Context for prototype development of TFX pipelines.
• Work with the Tensorflow Data Validation (TFDV) library to check and analyze input data.
• Utilize the Tensorflow Transform (TFT) library for scalable data preprocessing and feature transformations.
• Use the KerasTuner library for model hyperparameter tuning.
• Employ the Tensorflow Model Analysis (TFMA) library for model evaluation.

Módulo 2: Pipeline orchestration with TFX

Use the TFX CLI and Kubeflow UI to build and deploy TFX pipelines to a hosted AI Platform Pipelines instance on Google Cloud.

• Deploy a TensorFlow model trained using AI Platform Training to AI Platform Prediction.
• Perform advanced distributed hyperparameter tuning using CloudTuner and Cloud AI Platform Vizier.

Módulo 3: Custom components and CI/CD for TFX pipelines

Develop a CI/CD workflow with Cloud Build to build and deploy a TFX Pipeline.

• Integrate Github trigger to trigger Cloud Build CI/CD workflow for a TFX pipeline.

Módulo 4: ML Metadata with TFX

Access and analyze pipeline artifacts in ML Metadata store.

Módulo 5: Continuous Training with multiple SDKs, KubeFlow & AI Platform Pipelines

Perform continuous training with Scikit-learn and AI Platform Pipelines

• Perform continuous training with PyTorch and AI Platform Pipelines
• Perform continuous training with XGBoost and AI Platform Pipelines
• Perform continuous training with TensorFlow and AI Platform Pipelines

Módulo 6: Continuous Training with Cloud Composer

Perform continuous training with Cloud Composer

Módulo 7: ML Pipelines with MLflow

Manage Machine Learning lifecycle with MLflow

Módulo 8: Summary

Summarize the course

Documentación oficial para el curso ML Pipelines on Google Cloud.

• Formador certificado por Google Cloud.
• Más de 5 años de experiencia profesional.
• Más de 4 años de experiencia docente.
• Profesional activo en empresas del sector IT.