Machine Learning on Google Cloud

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 work?ow of building an ML model.

• Describe MLOps and work?ow 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.

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.

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.

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.

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 Data?ow 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.

• 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, Data?ow, Vertex AI Vizier for hyperparameter tuning, and TensorFlow/Keras

Prerequisites

• Some familiarity with basic machine learning concepts

• Basic proficiency with a scripting language - Python preferred