Machine Learning on Google Cloud Training in New Zealand

  • Delivery Method: Online Instructor-Led / Classroom Based / Onsite
  • Participation Model: Public Training / Private / In-House Training
  • Duration: 5 Days
  • Level: Intermediate
  • Price: From NZD 8,800 +TAX
  • Upcoming Date:
  • UK & New Zealand Based Global Training Provider

This course provides a comprehensive introduction to the Artificial Intelligence (AI) and Machine Learning (ML) capabilities available on Google Cloud. Participants explore the data-to-AI lifecycle through AI foundations, AI development, and AI solutions.

Throughout the programme, learners examine the technologies, products, and tools used to develop Machine Learning models, ML pipelines, and Generative AI projects. Participants learn how to create AutoML models without writing code, build BigQuery ML models with SQL, and configure custom training jobs on Vertex AI using Keras and TensorFlow.

The course also covers data quality, exploratory data analysis, data preprocessing, feature engineering, model evaluation and optimisation, deployment, prediction, model monitoring, and MLOps.

Generative AI concepts are also introduced, including Large Language Models (LLMs), relevant APIs, and the generative AI capabilities available for AI development on Google Cloud.

Google Cloud Products and Technologies

The course covers a range of Google Cloud products and related technologies, including:

  • Vertex AI
  • AutoML
  • BigQuery ML
  • Vertex AI Pipelines
  • TensorFlow
  • Keras
  • Model Garden
  • Generative AI Studio
  • Large Language Model (LLM) APIs
  • Natural Language API
  • Vertex AI Workbench
  • Vertex AI Feature Store
  • Vertex AI Vizier
  • Dataplex
  • Analytics Hub
  • Data Catalog
  • Vertex AI TensorBoard
  • Dataflow
  • Dataprep

We can organize this training at your preferred date and location.
HAVELSAN
ASELSAN
PepsiCo Türkiye
JTI TÜTÜN ÜRÜNLERİ SANAYİ A.Ş.
ING Bank
DHL
MASTERCARD
Huawei
TotalEnergies
Mercedes-Benz Türk A.Ş.
Goodyear
FORD OTOMOTIV SANAYI A.S.
3M COMPANY
Schneider Electric
Central Bank of Azerbaijan
BP Petrolleri
Procter & Gamble (P&G)
Qatar Foundation
Bank Of China
GlaxoSmithKline (GSK)
MORGAN STANLEY & CO Inc
Novo Nordisk
DEUTSCHE BANK
Azerbaycan Cumhuriyeti Emek ve Ahalinin Sosyal Güvenliği Bakanlığı
ACCENTURE

Prerequisites

To gain the most from this course, participants should have:

  • Some familiarity with fundamental Machine Learning concepts,
  • Basic proficiency with a scripting language, preferably Python.

Who Should Attend

This course is particularly suitable for:

  • Aspiring Machine Learning Data Scientists and Engineers,
  • Data Scientists,
  • ML Developers,
  • ML Engineers,
  • Data Engineers,
  • Data Analysts,
  • Technical professionals developing AI/ML solutions on Google Cloud,
  • Google and partner field personnel supporting customers in these roles.

What You Will Learn

By the end of this course, participants will be able to:

  • Describe the main Google Cloud technologies used to build ML models, ML pipelines, and Generative AI projects,
  • Determine when AutoML and BigQuery ML are appropriate,
  • Create managed datasets in Vertex AI,
  • Work with features in Vertex AI Feature Store,
  • Explain the roles of Analytics Hub, Dataplex, and Data Catalog,
  • Identify techniques for improving model performance,
  • Work with notebook environments in Vertex AI Workbench,
  • Create custom training jobs and understand deployment using Docker containers,
  • Explain Batch and Online Predictions,
  • Understand Model Monitoring,
  • Improve data quality and perform exploratory data analysis,
  • Build and train Supervised Learning models,
  • Evaluate and optimise models using Loss Functions and Performance Metrics,
  • Create repeatable and scalable Training, Evaluation, and Test datasets,
  • Implement ML models using TensorFlow and Keras,
  • Explain the benefits of Feature Engineering,
  • Understand Vertex AI Model Monitoring and Vertex AI Pipelines.

Training Outline

Section 1 – Introduction to AI and Machine Learning on Google Cloud

  • Understanding the Google Cloud AI/ML framework
  • Identifying the major components of Google Cloud infrastructure
  • Understanding how Data and ML products support the data-to-AI lifecycle
  • Building an ML model with BigQuery ML
  • Exploring different approaches to building ML models on Google Cloud
  • Comparing Pre-trained APIs, AutoML, and Custom Training
  • Analysing text with the Natural Language API
  • Understanding the ML model development workflow
  • MLOps and workflow automation on Google Cloud
  • Building an end-to-end ML model with AutoML on Vertex AI
  • Introduction to Generative AI
  • Understanding Large Language Models (LLMs)
  • Using Generative AI capabilities during AI development
  • Exploring Google Cloud AI solutions and embedded Generative AI capabilities
  • Hands-On Labs
  • Module Quizzes
  • Module Readings

Section 2 – Launching into Machine Learning

  • Techniques for improving Data Quality
  • Performing Exploratory Data Analysis
  • Building and training Supervised Learning models
  • Understanding AutoML
  • Building, training, and deploying ML models without writing code
  • Understanding BigQuery ML and its benefits
  • Working with Loss Functions
  • Evaluating models with Performance Metrics
  • Optimising model performance
  • Identifying and mitigating common Machine Learning problems
  • Creating repeatable and scalable Training, Evaluation, and Test datasets
  • Hands-On Labs
  • Module Quizzes
  • Module Readings

Section 3 – TensorFlow on Google Cloud

  • Creating Machine Learning models with TensorFlow and Keras
  • Understanding the main TensorFlow components
  • Using tf.data to work with data and large datasets
  • Building models with tf.keras preprocessing layers
  • Using the Keras Sequential API for straightforward model architectures
  • Using the Keras Functional API for more advanced models
  • Training models at scale with Vertex AI Training Service
  • Deploying and productionalising ML models
  • Hands-On Labs
  • Module Quizzes
  • Module Readings

Section 4 – Feature Engineering

  • Understanding Vertex AI Feature Store
  • Identifying the characteristics of effective features
  • Using tf.keras.preprocessing utilities with Image Data
  • Preprocessing Text Data
  • Preprocessing Sequence Data
  • Performing Feature Engineering with BigQuery ML
  • Feature Engineering with Keras
  • Feature Engineering with TensorFlow
  • Understanding the impact of features on model performance
  • Hands-On Labs
  • Module Quizzes
  • Module Readings

Section 5 – Machine Learning in the Enterprise

  • Tools for Data Management and Data Governance
  • Comparing Data Preprocessing approaches
  • Overview of Dataflow
  • Preprocessing with Dataprep
  • Using SQL for preprocessing tasks
  • Comparing AutoML, BigQuery ML, and Custom Training
  • Selecting an appropriate ML development approach for different use cases
  • Hyperparameter Tuning with Vertex AI Vizier
  • Improving model performance
  • Understanding Prediction workflows
  • Model Monitoring
  • Managing ML models with Vertex AI
  • Understanding the benefits of Vertex AI Pipelines
  • Best practices for Model Deployment and Serving
  • Model Monitoring best practices
  • Best practices for Vertex AI Pipelines
  • Organising ML artifacts
  • Hands-On Labs
  • Module Quizzes
  • Module Readings

Why Choose Us

Experience Machine Learning on Google Cloud in New Zealand through Bilginç IT Academy's live and interactive virtual classroom environment. Join a Public course as an individual delegate or arrange a dedicated Private / In-house online training program exclusively for your organization.

  • Delivery Method: Online Instructor-Led
  • Participation Model: Public / Private (In-house)
  • Live and Interactive Training: Connect with your instructor in real time and actively participate through discussions, Q&A sessions, practical exercises, and group activities.
  • Flexible Participation: Join the training from your home, office, or any location with a suitable internet connection.
  • Expert Trainer Network: Learn from experienced trainers with strong industry backgrounds and practical field expertise.
  • Over 30 Years of Training Expertise: Benefit from Bilginç IT Academy's professional training experience since 1995.
  • Worldwide Access: Join our live virtual classrooms from New Zealand or anywhere else in the world, or arrange a dedicated online training program for your organization.

Experience Machine Learning on Google Cloud through face-to-face Classroom Based training in New Zealand. Training can be delivered as a Public course open to individual delegates or as a dedicated Private / In-house class for your organization.

  • Delivery Method: Classroom Based
  • Participation Model: Public / Private (In-house)
  • Face-to-Face Learning: Interact directly with your instructor and fellow delegates in an engaging classroom environment.
  • Experienced Trainers: Learn from specialists with extensive industry experience and practical real-world knowledge.
  • Professional Training Environment: Attend training in comfortable, well-equipped classrooms designed to support effective learning.
  • Practical Learning: Depending on the course, reinforce your knowledge through hands-on exercises, scenarios, case studies, and instructor-led activities.

Arrange Machine Learning on Google Cloud in New Zealand as a dedicated Onsite training program for your organization. Bilginç IT Academy trainers can deliver the training at your office or another location of your choice, with the program planned around your team's requirements and business objectives.

  • Delivery Method: Onsite
  • Participation Model: Private (In-house)
  • Training at Your Preferred Location: Organize the training at your company's office or another location selected by your organization.
  • Tailored Course Content: Adapt the training program to your projects, team structure, existing skill levels, and specific business requirements.
  • Team-Focused Learning: Develop your team around a shared knowledge base while strengthening internal collaboration and knowledge transfer.
  • Flexible Scheduling: Plan the training dates, location, and program according to your organization's operational requirements.
  • Worldwide Onsite Delivery: Arrange the training in New Zealand or at another preferred location worldwide. Bilginç IT Academy trainers can travel to your selected location to deliver the dedicated training program.
Contact us for more detail about our trainings and for all other enquiries!

Machine Learning on Google Cloud Training Course in New Zealand Schedule

Join our public courses in our New Zealand facilities. Private class trainings will be organized at the location of your preference, according to your schedule.

We can organize this training at your preferred date and location.
08 November 2026 (5 Days)
Auckland, Wellington, Christchurch
NZD 8,800 +TAX
14 November 2026 (5 Days)
Auckland, Wellington, Christchurch
NZD 8,800 +TAX
17 November 2026 (5 Days)
Auckland, Wellington, Christchurch
NZD 8,800 +TAX
18 November 2026 (5 Days)
Auckland, Wellington, Christchurch
NZD 8,800 +TAX
26 November 2026 (5 Days)
Auckland, Wellington, Christchurch
NZD 8,800 +TAX
12 February 2027 (5 Days)
Auckland, Wellington, Christchurch
NZD 8,800 +TAX
01 April 2027 (5 Days)
Auckland, Wellington, Christchurch
NZD 8,800 +TAX
09 May 2027 (5 Days)
Auckland, Wellington, Christchurch
NZD 8,800 +TAX

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