Machine Learning on Google Cloud

Name: Machine Learning on Google Cloud
Price: 29950.00 ZAR

What is machine learning, and what problems can it solve? Learn the ins and outs of how it is implemented and the role that tools such as Vertex AI, BQML, and Dataflow play on this five-day course.

5 day course

Supporting material

Google Cloud Partner of the Year

Private

A private training session for your team. Groups can be of any size, at a location of your choice including our training centres.

As a Google Cloud partner, we pride ourselves in providing world-leading Cloud-based training solutions – led by expert practitioners – that give our clients the skills to succeed.

Over five days, you’ll learn how to build Vertex AI AutoML models without writing a single line of code and build BigQuery ML models knowing basic SQL.

You’ll also be taught how to create Vertex AI custom training using containers; use Feature Store for data management and governance; determine appropriate data preprocessing options for your use case and write distributed ML models that scale in TensorFlow. Finally, you’ll discover how to leverage best practices for implementing machine learning on Google Cloud.

Our Machine Learning on Google Cloud course is available as a private training session that can be delivered via Virtual Classroom or at a location of your choice in South Africa.

Course overview

Who should attend:

This intermediate-level course is primarily intended for the following participants:

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 / Kerass

What you'll learn:

By the end of this course, you will be able to:

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

Prerequisites

To get the most out of this course, you should have:

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

Course agenda

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 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 how to improve data quality
Perform exploratory data analysis
Describe Big Query ML and its benefits
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
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

Book this course

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