Discover how to modernize, manage, and observe applications at scale using Google Kubernetes Engine Enterprise. This course uses lectures and hands-on labs to help you explore and deploy using Google Kubernetes Engine (GKE), GKE Fleets, Cloud Service Mesh, and Config Controller capabilities that will enable you to work with modern applications, even when they are split among multiple clusters hosted by multiple providers.
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The most automated and scalable managed Kubernetes platform.
- Learn how to create and deploy containerized applications on Google Kubernetes Engine (GKE).
- This GCP-GSGKE: Getting Started with Google Kubernetes Engine course features a combination of lectures, demos, and hands-on labs to help you explore and deploy solution elements including infrastructure components like pods, and containers.
Limited Time Offer: Get up to 45% OFF selected Google Cloud courses in H2 2025 via our Google Cloud Certified program.
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Optimize Your Infrastructure Management with Terraform for Google Cloud.
This course provides an introduction to using Terraform for Google Cloud. It enables learners to describe how Terraform can be used to implement infrastructure as a code and to apply some of its key features and functionalities to create and manage Google Cloud infrastructure. Learners will get hands-on practice building Google Cloud resources using Terraform.
Limited Time Offer: Get up to 45% OFF selected Google Cloud courses in H2 2025 via our Google Cloud Certified program.
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Many traditional enterprises use legacy systems and applications that often struggle to achieve the scale and speed needed to meet modern customer expectations. Business leaders and IT decision makers constantly have to choose between maintenance of legacy systems and investing in innovative new products and services.
This course explores the challenges of an outdated IT infrastructure and how businesses can modernize it using cloud technology. It begins by exploring the different compute options available in the cloud and the benefits of each, before turning to application modernization and Application Programming Interfaces (APIs). The course also considers a range of Google Cloud solutions that can help businesses to better develop and manage their systems, such as Compute Engine, App Engine, and Apigee.
This is the third course in the Cloud Digital Leader series. At the end of this course, enroll in the Understanding Google Cloud Security and Operations course
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This course is an introduction to data analytics on Google Cloud.
It is designed for learners who have no prior experience with data analytics or Google Cloud. The course covers the basics of data analysis, including collection, storage, exploration, visualization, and sharing. It also introduces learners to Google Cloud’s data analytics tools and services.
Through video lectures, demos, quizzes, and hands-on labs, the course demonstrates how to go from raw data to impactful visualizations and dashboards.
Limited Time Offer: Get up to 45% OFF selected Google Cloud courses in H2 2025 via our Google Cloud Certified program.
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In this course, you learn about data engineering on Google Cloud, the roles and responsibilities of data engineers, and how those map to offerings provided by Google Cloud. You also learn about ways to address data engineering challenges.
Upskill in introduction to data engineering with 30% off – limited-time price: RM1680
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Cloud technology on its own only provides a fraction of the true value to a business; When combined with data–lots and lots of it–it has the power to truly unlock value and create new experiences for customers. In this course, you’ll learn what data is, historical ways companies have used it to make decisions, and why it is so critical for machine learning. This course also introduces learners to technical concepts such as structured and unstructured data. database, data warehouse, and data lakes. It then covers the most common and fastest growing Google Cloud products around data.
This is the second course in the Cloud Digital Leader series. At the end of this course, enroll in the Infrastructure and Application Modernization with Google Cloud course.
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In this course, you explore generative AI (Gen AI) use cases across specific industries using Google Cloud.
After discussing generative AI in general, you will do a deep dive into two generative AI use cases and then explore your own use case within the same framework. This course targets professionals across different roles, including business analysts, data scientists, software developers, business users, and decision-makers, who are specifically interested in leveraging generative AI for business solutions.
Limited Time Offer: Get up to 45% OFF selected Google Cloud courses in H2 2025 via our Google Cloud Certified program.
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Dataplex is an intelligent data fabric that enables organizations to centrally discover, manage, monitor, and govern their data across data lakes, data warehouses, and data marts. You can use Dataplex to build a data mesh architecture to decentralize data ownership among domain data owners. In this course, you will learn how to discover, manage, monitor, and govern your data across data lakes, data warehouses, and data marts through guided lectures and independent exercises using sample data.
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Get the best of on-premises and Apigee cloud advantages.
Learn how to install and manage Google Cloud’s Apigee API Platform in a hybrid cloud. This course uses lectures, hands-on labs, and supplemental resources to show you how to design, install, manage, and scale your Apigee API Platform.
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This course introduces the artificial intelligence (AI) and machine learning (ML) offerings on Google Cloud that support the data-to-AI lifecycle through AI foundations, AI development, and AI solutions.
It explores the technologies, products, and tools available to build an ML model, an ML pipeline, and a generative AI project. You learn how to build AutoML models without writing a single line of code; build BigQuery ML models using SQL, and build Vertex AI custom training jobs by using Keras and TensorFlow. You also explore data preprocessing techniques
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This GCP-MLOF: MLOps (Machine Learning Operations) Fundamentals course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production.
Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.
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In this GCP-MPGC: ML Pipelines on Google Cloud course, you will learn about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. The first few modules discuss pipeline components, pipeline orchestration with TFX, how you can automate your pipeline through CI/CD, and how to manage ML metadata.
Then we will discuss how to automate and reuse ML pipelines across multiple ML frameworks such as tensorflow, pytorch, scikit learn, and xgboost. You will also learn how to use Cloud Composer to orchestrate your continuous training pipelines, and MLflow for managing the complete machine learning life cycle.
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In this course you will learn how to translate various concepts in Amazon Redshift to the analogous concepts in BigQuery. You will learn how the high-level architectures of Amazon Redshift and BigQuery compare, understand differences in how to configure datasets and tables, map data types in Amazon Redshift to data types in BigQuery, understand schema mapping from Amazon Redshift to BigQuery, optimize your new schemas in BigQuery, and do a high-level comparison of SQL dialects in Amazon Redshift and BigQuery.
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In this course you will learn how to translate various concepts in Teradata to the analogous concepts in BigQuery. You will learn how the high-level architectures of Teradata and BigQuery compare, understand differences in how to configure datasets and tables, map data types in Teradata to data types in BigQuery, understand schema mapping from Teradata to BigQuery, optimize your new schemas in BigQuery, and do a high-level comparison of SQL dialects in Teradata and BigQuery
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This course is intended to give architects, engineers, and developers the skills required to help enterprise customers architect, plan, execute, and test database migration projects. Through a combination of presentations, demos, and hands-on labs participants move databases to GCP while taking advantage of various GCP services.
This course covers how to move on-premises, enterprise databases like SQL Server to Google Cloud (Compute Engine and Cloud SQL) and Oracle to Google Cloud bare metal.
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The next generation of Dataflow: Dataflow Prime, Dataflow Go, and Dataflow ML.
This training is intended for big data practitioners who want to further their understanding of Dataflow in order to advance their data processing applications.
Beginning with foundations, this training explains how Apache Beam and Dataflow work together to meet your data processing needs without the risk of vendor lock-in. The section on developing pipelines covers how you convert your business logic into data processing applications that can run on Dataflow.
This training culminates with a focus on operations, which reviews the most important lessons for operating a data application on Dataflow, including monitoring, troubleshooting, testing, and reliability
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In this course, you learn about Cloud Spanner. You will get an introduction to Cloud Spanner and understand how it differs from other database products. You also learn when and how to use Cloud Spanner to solve your relational database needs at scale.
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Vertex AI Agent Builder lets developers, even those with limited machine learning skills, tap into the power of Google’s foundation models, search expertise, and conversational AI technologies to create enterprise-grade generative AI applications.
In this course, you learn how to use Vertex AI Agent Builder to create search engines and chat applications. You will then explore how to integrate these search engineers and chat applications into your own applications.
Finally, you learn how to manage the tools built in Vertex AI Agent Builder in production.
Limited Time Offer: Get up to 45% OFF selected Google Cloud courses in H2 2025 via our Google Cloud Certified program.
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As generative AI becomes more common, the ability to interact with large language models is shifting from niche knowledge to a necessary skill across many different industries and roles.
In this course, you will learn the fundamentals of prompting large language models and exploring further techniques for improving the output from large language models. You will also explore similar concepts when working with multimodal models such as Gemini Vision Pro.
Limited Time Offer: Get up to 45% OFF selected Google Cloud courses in H2 2025 via our Google Cloud Certified program.
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This GCP: GCSO: Understanding Google Cloud Security and Operations course examines cost management, security, and operations in the cloud. First, it explores how businesses can choose to maintain some or none of their own infrastructure by purchasing IT services from a cloud provider. Next, it explains how the responsibility of data security is shared between the cloud provider and the business, and explores the defense-in-depth security built into Google Cloud.
Finally, it covers how IT teams and business leaders need to rethink IT resource management in the cloud and how Google Cloud resource monitoring tools can help them to maintain control and visibility over their cloud environment.
This is the fourth and final course in the Cloud Digital Leader series.
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Discover how to modernize, manage, and observe applications using Kubernetes—whether the application is deployed on-premises or on Google Cloud.
This course uses lectures and hands-on labs to help you explore and deploy using Kubernetes Engine (GKE), GKE Connect, Istio service mesh, and Anthos Config Management capabilities that will enable you to work with modern applications, even when they are split among multiple clusters hosted by multiple providers, or on-premises.
This is a continuation of Architecting with GKE, and you’ll need hands-on experience with the technologies covered in that course in order to benefit from this course.
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Learn about Google Cloud’s computing and storage services available, including Compute Engine, Google Kubernetes Engine, App Engine, Cloud Storage, Cloud SQL, and BigQuery.
This GCPCIN: Google Cloud Fundamentals: Core Infrastructure course uses lectures, demos, and hands-on labs to give you an overview of Google Cloud products and services so that you can learn the value of Google Cloud and how to incorporate cloud-based solutions into your business strategies
Enroll now for RM1,320 – enjoy 45% off this entry-level course in Google Cloud fundamentals.
Learn more about Multicloud Strategies in Malaysia: Adopting Multicloud Strategies in Malaysia: A 2024 Roadmap
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Data Engineers design solutions that ensure maximum flexibility and scalability, while meeting all required security controls.
Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hand-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning.
This Google Cloud course covers structured, unstructured, and streaming data.
Limited Time Offer: Get up to 45% OFF selected Google Cloud courses in H2 2025 via our Google Cloud Certified program.
