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 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