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Shaping the future of IT skills

Maximising IT performance through learning

Machine Learning on Google Cloud

WGAC-GGL-MLGC

Google
Open

Machine Learning on Google Cloud

18 Jul 2022 - 5 days

German

CET UTC+01:00

£2,810

Open

Machine Learning on Google Cloud

15 Aug 2022 - 5 days

German

CET UTC+01:00

£2,810

Open

Machine Learning on Google Cloud

19 Sep 2022 - 5 days

German

CET UTC+01:00

£2,810

Open

Machine Learning on Google Cloud

24 Oct 2022 - 5 days

German

CET UTC+01:00

£2,810

Open

Machine Learning on Google Cloud

05 Dec 2022 - 5 days

German

CET UTC+01:00

£2,810

Description

Show Tabs
Introduction
Module 1: How Google Does Machine Learning
  • Describe the Vertex AI Platform and how it is used to quickly build, train, and deploy AutoML machine learning models without writing a single line of code.
  • Describe best practices for implementing machine learning on Google Cloud.
  • Develop a data strategy around machine learning
  • Examine use cases that are then reimagined through an ML lens
  • Leverage Google Cloud Platform tools and environment to do ML
Module 2: Launching into Machine Learning
  • Describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code.
  • Describe Big Query ML and its benefits.
  • 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.
  • Mitigate common problems that arise in machine learning.
  • Create repeatable and scalable training, evaluation, and test datasets.
Module 3:TensorFlow on Google Cloud
  • Create TensorFlow and Keras machine learning models.
  • Describe TensorFlow key components.
  • Use the tf.data library to manipulate data and large datasets.
  • Build a ML model using tf.keras preprocessing layers.
  • Use the Keras Sequential and Functional APIs for simple and advanced model creation. Understand how model subclassing can be used for more customized models.
Module 4: Feature Engineering
  • Describe Vertex AI Feature Store.
  • Compare the key required aspects of a good feature.
  • Combine and create new feature combinations through feature crosses.
  • Perform feature engineering using BQML, Keras, and TensorFlow.
  • Understand how to preprocess and explore features with Cloud Dataflow and Cloud Dataprep.
  • Understand and apply how TensorFlow transforms features.
Module 5: Machine Learning in the Enterprise
  • Understand the tools required for data management and governance
  • Describe the best approach for data preprocessing - from providing an overview of DataFlow and DataPrep to using SQL for preprocessing tasks.
  • Explain how AutoML, BQML, and custom training differ and when to use a particular framework.
  • Describe hyperparameter tuning using Vertex Vizier and how it can be used to improve model performance.
  • Explain prediction and model monitoring and how Vertex AI can be used to manage ML models.
  • Describe the benefits of Vertex AI Pipelines
Prerequisites & Audience
  • Some familiarity with basic machine learning concepts.
  • Basic proficiency with a scripting language - Python preferred.
Course Benefits
  • 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

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