GOOGLE CLOUD

Machine Learning on Google Cloud

This course teaches you how to build Vertex AI AutoML models without writing a single line of code; build BigQuery ML models knowing basic SQL; create Vertex AI custom training jobs you deploy using containers (with little knowledge of Docker0; use Feature Store for data management and governance; use feature engineering for model improvement; determine the appropriate data preprocessing options for your use case; write distributed ML models that scale in TensorFlow; and leverage best practices to implement machine learning on Google Cloud. Learn all this and more!

What you will learn

  • Build, train, and deploy a machine learning model without writing a single line of code using Vertex AI AutoML.

  • Understand when to use AutoML and Big Query ML.

  • Create Vertex AI managed datasets.

  • Add features to a Feature Store.

  • Describe Analytics Hub, Dataplex, and Data Catalog.

  • Describe hyperparameter tuning using Vertex Vizier and how it can be used to improve model performance.

  • Create a Vertex AI Workbench User-Managed Notebook, build a custom training job, and then deploy it using a Docker container.

  • Describe batch and online predictions and model monitoring.

  • 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.

  • Create repeatable and scalable train, eval, and test datasets.

  • Implement ML models using TensorFlow/Keras.

  • Describe how to represent and transform features.

  • Understand the benefits of using feature engineering.

  • Explain Vertex AI Pipelines.

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

Level

  • Intermediate

Duration

  • 5 x 8 hour sessions

Prerequisites

  • Some familiarity with basic machine learning concepts

  • Basic proficiency with a scripting language, preferably Python

Language

  • Delivered in English

Course TOPICS

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.

Ref: T-GCPML-I-03

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