Introduction to AI and Machine Learning on Google Cloud
- Recognize the AI/ML framework on Google Cloud.
- Identify the major components of Google Cloud infrastructure.
- Define the data and ML products on Google Cloud and how they support the data-to-AI lifecycle.
- Build an ML model with BigQueryML to bring data to AI.
- Define different options to build an ML model on Google Cloud.
- Recognize the primary features and applicable situations of pre-trained APIs, AutoML, and custom training.
- Use the Natural Language API to analyze text.
- Define the workflow of building an ML model.
- Describe MLOps and workflow automation on Google Cloud.
- Build an ML model from end-to-end by using AutoML on Vertex AI.
- Define generative AI and large language models.
- Use generative AI capabilities in AI development.
- Recognize the AI solutions and the embedded generative AI features.
- Hands-On Labs
- Module Quizzes
- Module Readings
Launching into Machine Learning
- Describe how to improve data quality.
- Perform exploratory data analysis.
- Build and train supervised learning models.
- Describe AutoML and how to build, train, and deploy an ML model without writing a single line of code.
- Describe BigQuery ML and its benefits.
- Optimize and evaluate models by using loss functions and performance metrics.
- Mitigate common problems that arise in machine learning.
- Create repeatable and scalable training, evaluation, and test datasets.
- Hands-On Labs
- Module Quizzes
- Module Readings
TensorFlow on Google Cloud
- Create TensorFlow and Keras machine learning models.
- Describe the TensorFlow main components.
- Use the tf.data library to manipulate data and large datasets.
- Build a ML model that uses tf.keras preprocessing layers.
- Use the Keras Sequential and Functional APIs for simple and advanced model creation.
- Train, deploy, and productionalize ML models at scale with the Vertex AI Training Service.
- Hands-On Labs
- Module Quizzes
- Module Readings
Feature Engineering
- Describe Vertex AI Feature Store.
- Compare the key required aspects of a good feature.
- Use tf.keras.preprocessing utilities for working with image data, text data, and sequence data.
- Perform feature engineering by using BigQuery ML, Keras, and TensorFlow.
- Hands-On Labs
- Module Quizzes
- Module Readings
Machine Learning in the Enterprise
- Understand the tools required for data management and governance.
- Describe the best approach for data preprocessing: From providing an overview of Dataflow and Dataprep to using SQL for preprocessing tasks.
- Explain how AutoML, BigQuery ML, and custom training differ and when to use a particular framework.
- Describe hyperparameter tuning by using Vertex AI Vizier to improve model performance.
- Explain prediction and model monitoring and how Vertex AI can be used to manage ML models.
- Describe the benefits of Vertex AI Pipelines.
- Describe best practices for model deployment and serving, model monitoring, Vertex AI Pipelines, and artifact organization.
- Hands-On Labs
- Module Quizzes
- Module Readings
