Machine Learning on Google Cloud

Overview

Overview

Duration 5 days
Course Time 9.00am - 5.00pm
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This course is delivered by CloudMile.

This course will teach participants how to build Vertex AI AutoML models without writing a single line of code, build BigQuery ML models knowing basic SQL and create Vertex AI custom training jobs they can deploy using containers (with little knowledge of Docker). 

Participants will also use Feature Store for data management and governance, use feature engineering for model improvement, determine the appropriate data preprocessing options for their use case, write distributed ML models that scale in TensorFlow and leverage best practices to implement machine learning on Google Cloud.

Key Takeaways

Key Takeaways

At the end of this course, the participiants 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
  • Optimise 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 Should Attend

Who Should Attend

This course is ideal for AI Engineers, Data Engineers, Infrastruccture Engineers and Infrastructure Architects.

Pre-requisites
To get the most out of this course, participants should:

• Have completed Google Cloud Big Data and ML Fundamentals course
• Some familiarity with basic machine learning concepts
• Basic proficiency with a scripting language, preferably Python

ICT and SS Competency Framework

ICT and SS Competency Framework

As part of the ICTCF, this course falls under the Data Science & AI and ICT Infrastructure clusters and tagged to the following competencies:
  • Data Science & AI: Machine Learning
  • ICT Infrastructure: Cloud Infrastructure

The course is mapped to the following job roles:
  • AI Engineer
  • Data Engineer
  • Infrastructure Architects
  • Infrastructure Engineers



Course Structure

Course Structure

This course is delivered via Virtual Instructor-Led Training. There is technical assessment which includes labs and quiz sessions as part of this course.

Instructors

Instructors


Fees

Fees


Full Fee

Full course fee

S$3000

7% GST on nett course fee

S$210

Total nett course fee payable, including GST S$3210




How To Register

How To Register


Agency-sponsored

Step 1 Apply through your organisation's training request system

Step 2 Your organisation's training request system (or relevant HR staff) confirms your organisation's approval for you to take the course.

Your organisation will send registration information to the academy.

Organisation HR L&D or equivalent staff can click here for details of the registration submission process.


Step 3 The Digital Academy will inform you whether you have been successful in enrolment.