Machine Learning on Google Cloud

Who this course is for?

• Aspiring machine learning data analysts, data scientists and data engineers
• Learners who want exposure to ML using Vertex AI AutoML, BQML, Feature Store, Workbench, Dataflow, Vizier for hyperparameter tuning, and TensorFlow/Keras

Prerequisite

• Some familiarity with basic machine learning concepts
• Basic proficiency with a scripting language, preferably Python

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.
  • Learn from Google’s experience to avoid common pitfalls.
  • Carry out data science tasks in online collaborative notebooks.
• 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.
  • Use tf.keras.preprocessing utilities for working with image data, text data, and sequence data.
  • Train, deploy, and productionalize ML models at scale with Cloud AI Platform.
• 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 BigQuery ML, Keras, and TensorFlow.
  • Understand how to preprocess and explore features with Dataflow and Dataprep by Trifacta.
  • 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, BigQuery ML, 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.