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.

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Prices & Delivery methods

Online Training
Modality: L

Duration 5 days

Price
  • Eastern Europe: 3,250.— €
Classroom Training
Modality: C

Duration 5 days

Price
  • Eastern Europe: 3,250.— €
  • Greece: 3,385.— €

Schedule

English

Time zone: Central European Summer Time (CEST)

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 Show training days 4 days This is a FLEX course. Time zone: Gulf Standard Time (GST)
Online Training This is a FLEX course. Time zone: Gulf Standard Time (GST)
Online Training This is a FLEX course. Time zone: Eastern European Summer Time (EEST)

3 hours difference

Online Training Show training days 4 days This is a FLEX course. Time zone: Gulf Standard Time (GST)

6 hours difference

Online Training Time zone: UTC+8

7 hours difference

Online Training Time zone: Central Standard Time (CST)
Online Training Time zone: Central Standard Time (CST)
Online Training Time zone: UTC+8

9 hours difference

Online Training Time zone: Pacific Daylight Time (PDT)
Online Training Time zone: Pacific Daylight Time (PDT)
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. All FLEX courses are also Instructor-led Online Trainings (ILO).

Europe

Italy

Rome This is a FLEX course.   Time zone: Europe/Rome Enroll:
for online training
for classroom training
This is a FLEX course, which is delivered both virtually and in the classroom. All FLEX courses are also Instructor-led Online Trainings (ILO).