Compendium Centrum Edukacyjne

This course introduces the artificial intelligence (AI) and machine learning (ML) offerings on Google Cloud that support the data-to-AI lifecycle through AI foundations, 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.

Course objectives

• 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.

Audience

This class is primarily intended for the following participants:

• Aspiring machine learning data analysts, data scientists, and data engineers
• Learners who want exposure to ML and use Vertex AI, AutoML, BigQuery ML, Vertex AI Feature Store, Vertex AI Workbench, Dataflow, Vertex AI Vizier for hyperparameter tuning, and TensorFlow/Keras