Machine Learning on Google Cloud

GCPMLGC-QA
Cena na vyžádání

This course introduces the artificial intelligence (AI) and machine learning (ML) offerings on Google Cloud that support the data-to-AI lifecycle through AIfoundations, AI development, and AI solutions. It explores the technologies, products, and tools available to build an ML model, an ML pipeline, and a generative AI project. You learn how to build AutoML models without writing a single line of code; build BigQuery ML models using SQL, and build Vertex AI custom training jobs by using Keras and TensorFlow. You also explore data preprocessing techniques and feature engineering. ProductsVertex AIAutoMLBigQuery MLVertex AI PipelinesTensorFlowModel GardenGenerative AI StudioLarge language model (LLM) APIsNatural Language APIVertex AI WorkbenchVertex AI Feature StoreVizierDataplexAnalytics HubData CatalogTensorFlowVertex AI TensorBoardDataflowDataprepVertex AI Pipelines

Describe the technologies, products, and tools to build an ML model, an ML pipeline, and a Generative AI project.
Understand when to use AutoML and BigQuery ML.
Create Vertex AI-managed datasets.
Add features to the Vertex AI Feature Store.
Describe Analytics Hub, Dataplex, and Data Catalog.
Describe how to improve model performance.
Create Vertex AI Workbench user-managed notebook, build a custom training job, and deploy it by using a Docker container.
Describe batch and online predictions and model monitoring.
Describe how to improve data quality and explore your data.
Build and train supervised learning models.
Optimize and evaluate models by using loss functions and performance metrics.
Create repeatable and scalable train, eval, and test datasets.
Implement ML models by using TensorFlow or Keras.
Understand the benefits of using feature engineering.
Explain Vertex AI Model Monitoring and Vertex AI Pipelines.

Machine Learning on Google Cloud

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