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.

Follow On Courses

Prices & Delivery methods

Online Training

Duration
5 days

Price
  • 3,250.— €
Classroom Training

Duration
5 days

Price
  • Austria: 3,250.— €
  • Germany: 3,250.— €
  • Switzerland: CHF 3,190.—

Schedule

Instructor-led Online Training:   Course conducted online in a virtual classroom.
FLEX Classroom Training (hybrid course):   Course participation either on-site in the classroom or online from the workplace or from home.

English

Time zone: Central European Time (CET)   ±1 hour

Online Training
Classroom option: Cairo, Egypt
Time zone: Eastern European Summer Time (EEST)
Online Training 4 days Time zone: Central European Summer Time (CEST)
Online Training 4 days Time zone: Central European Time (CET)

2 hours difference

Online Training
Classroom option: Dubai, United Arab Emirates
Time zone: Gulf Standard Time (GST)
Online Training
Classroom option: Dubai, United Arab Emirates
View the exact training days 4 days Time zone: Gulf Standard Time (GST)
Online Training
Classroom option: Dubai, United Arab Emirates
Time zone: Gulf Standard Time (GST)
Online Training
Classroom option: Cairo, Egypt
Time zone: Eastern European Summer Time (EEST)

3 hours difference

Online Training
Classroom option: Dubai, United Arab Emirates
View the exact training days 4 days Time zone: Gulf Standard Time (GST)

6 hours difference

Online Training Time zone: Eastern Daylight Time (EDT)
Online Training Time zone: Eastern Daylight Time (EDT)

7 hours difference

Online Training Time zone: Central Standard Time (CST)
Online Training Time zone: Central Standard Time (CST)

9 hours difference

Online Training Time zone: Pacific Daylight Time (PDT)
Online Training Time zone: Pacific Daylight Time (PDT)
FLEX Classroom Training (hybrid course):   Course participation either on-site in the classroom or online from the workplace or from home.

Europe

Italy

Rome