This GCP-AMLTF: Advanced Machine Learning with TensorFlow on Google Cloud course will give you hands-on experience optimizing, deploying, and scaling a variety of production ML models. You’ll learn how to build recommendation systems
and scalable, accurate, and production-ready models for structured data, image data, time series, and natural language text.
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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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In this course, you’ll learn the fundamentals and best practices of SRE, the importance of adopting an SRE culture, and how SRE can improve collaboration between IT and business leaders—and help the entire organization succeed.
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The Logging, Monitoring and Observability in Google Cloud training course teaches participants techniques for monitoring, troubleshooting, and improving infrastructure and application performance in Google Cloud.
Learn how to monitor, troubleshoot, and improve your infrastructure and application performance. Guided by the principles of Site Reliability Engineering (SRE), this official Google Cloud course features a combination of lectures, demos, hands-on labs, and real-world case studies. In this course, you’ll gain experience with full-stack monitoring, real-time log management and analysis, debugging code in production, and profiling CPU and memory usage.
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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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What is machine learning, and what kinds of problems can it solve? Why are neural networks so popular right now? How can you improve data quality and perform exploratory data analysis? How can you set up a supervised learning problem and find a good, generalizable solution using gradient descent? In this course, you’ll learn how to write distributed machine learning models that scale in Tensorflow 2.x, perform feature engineering in BQML and Keras, evaluate loss curves and perform hyperparameter tuning, and train models at scale with Cloud AI Platform.
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If you are new to Google Workspace, this training will equip you with the skills you need to be productive in the workplace. Through a series of lectures, demonstrations, and hands-on activities, you will become proficient in the use of the following core Google Workspace applications: Gmail, Google Calendar, Google Drive, Google Docs, Google Sheets, Google Slides, Google Meet and Google Chat.
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This GCP-AWS: Google Cloud Fundamentals for AWS Professionals course is designed for AWS system administrators, solutions architects, and SysOps administrators who are familiar with AWS features and setup and want to gain experience configuring Google Cloud products immediately. This course uses lectures, demos, and hands-on labs to show you the similarities and differences between the two platforms and teach you about some basic tasks on Google Cloud.
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This course broadens the skills of the student who are required to implement and manage a Kubernetes-based Platform as a Service (PaaS) environment based on Red Hat OpenShift Container Platform on Power Systems. The course covers basic administration and configuration of Red Hat OpenShift Container Platform within a POWER processor-based server configured with IBM PowerVC. Hands-on exercises reinforce the lecture material, and allow students to use the Red Hat OpenShift Container Platform to work with images, applications, and manage a cluster.
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This course covers the steps to follow to update a Red Hat OpenShift Container Platform 4.3.18 environment on Power Systems to 4.3.23. Lessons learned, and hints and tips will be provided to help you perform the update successfully.
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This course guides students through the fundamentals of using IBM SPSS Statistics for typical data analysis. Students will learn the basics of reading data, data definition, data modification, data analysis, and presentation of analytical results. In addition to the fundamentals, students will learn shortcuts that will help them save time. This course uses the IBM SPSS Statistics Base; one section presents an add-on module, IBM SPSS Custom Tables.
