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

This class 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/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, 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, 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

Price (excl. tax)
  • 3,250.— €

Courseware language: English

Classroom Training

Duration 5 days

Price (excl. tax)
  • Germany: 3,250.— €
  • Switzerland: CHF 3,190.—

Courseware language: English

Schedule

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 Summer Time (CEST)   ±1 hour

Online Training This is a FLEX course. Time zone: Greenwich Mean Time (GMT)
Online Training This is a FLEX course. Time zone: British Summer Time (BST)
Online Training This is a FLEX course. Time zone: British Summer Time (BST)
FLEX Classroom Training (hybrid course):   Course participation either on-site in the classroom or online from the workplace or from home.

Germany

Hamburg
Online
Online
Hamburg Show training days 4 days
Berlin
Online
Munich
Online
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If you can't find a suitable date, don't forget to check our world-wide FLEX training schedule.