Machine Learning on Google Cloud (MLGC) - Outline

Detailed Course Outline

Module 1: How Google Does Machine Learning

• Describe the Vertex AI Platform and how it is used to quickly build, train, and deploy AutoML machine learning models without writing a single line of code.
• Describe best practices for implementing machine learning on Google Cloud.
• Develop a data strategy around machine learning
• Examine use cases that are then reimagined through an ML lens
• Leverage Google Cloud Platform tools and environment to do ML

Module 2: Launching into Machine Learning

• Describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code.
• Describe Big Query ML and its benefits.
• Describe how to improve data quality.
• Perform exploratory data analysis.
• Build and train supervised learning models.
• Optimize and evaluate models using loss functions and performance metrics.
• Mitigate common problems that arise in machine learning.
• Create repeatable and scalable training, evaluation, and test datasets.

Module 3:TensorFlow on Google Cloud

• Create TensorFlow and Keras machine learning models.
• Describe TensorFlow key components.
• Use the tf.data library to manipulate data and large datasets.
• Build a ML model using tf.keras preprocessing layers.
• Use the Keras Sequential and Functional APIs for simple and advanced model creation. Understand how model subclassing can be used for more customized models.

Module 4: Feature Engineering

• Describe Vertex AI Feature Store.
• Compare the key required aspects of a good feature.
• Combine and create new feature combinations through feature crosses.
• Perform feature engineering using BQML, Keras, and TensorFlow.
• Understand how to preprocess and explore features with Cloud Dataflow and Cloud Dataprep.
• Understand and apply how TensorFlow transforms features.

Module 5: 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, BQML, and custom training differ and when to use a particular framework.
• Describe hyperparameter tuning using Vertex Vizier and how it can be used 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