Machine Learning on Google Cloud (MLGC)

 

Course Overview

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!

Who should attend

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

Prerequisites

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

Course Objectives

  • 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.

Prices & Delivery methods

Online Training

Duration
5 days

Classroom Training

Duration
5 days

Click on town name or "Online Training" to book Schedule

Europe

Germany

Online Training This is a FLEX course in German language. Time zone: Central European Summer Time (CEST)
Online Training Time zone: Central European Summer Time (CEST) Course language: German
Online Training This is a FLEX course in German language. Time zone: Central European Summer Time (CEST)
Online Training Time zone: Central European Summer Time (CEST) Course language: German
Online Training This is a FLEX course in German language. Time zone: Central European Summer Time (CEST)
Online
Hamburg

Italy

Online Training This is a FLEX course in Italian language. Time zone: Central European Summer Time (CEST)
Online Training This is a FLEX course in Italian language. Time zone: Central European Summer Time (CEST)
Online Training This is a FLEX course in Italian language. Time zone: Central European Summer Time (CEST)

United Kingdom

Online Training Time zone: British Summer Time (BST) Course language: English
Online Training Time zone: British Summer Time (BST) Course language: English
Online Training Time zone: British Summer Time (BST) Course language: English
Online Training Time zone: Greenwich Mean Time (GMT) Course language: English
Guaranteed date:   iTLS will carry out all guaranteed training regardless of the number of attendees, exempt from force majeure or other unexpected events, like e.g. accidents or illness of the trainer, which prevent the course from being conducted.
Instructor-led Online Training:   This computer icon in the schedule indicates that this date/time will be conducted as Instructor-Led Online Training.
This is a FLEX course, which is delivered both virtually and in the classroom.