Course Overview
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.
What are the skills covered
- 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.
Who should attend this course
- 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 forhyperparameter tuning, and TensorFlow/Keras
Course Curriculum
Course Modules
Exam & Certification
Google Professional Machine Learning Engineer
