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.
-
-
-30%
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.
Are you currently retrenched? If yes, check out our PERKESO EIS: Get Back into the Workforce through Upskilling program.
-
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.
-
-30%
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
What is cloud technology or data science? More importantly, what can it do for you, your team, and your business? If you want to learn about cloud technology so you can excel in your role and help build the future of your business, then this introductory course on digital transformation is for you.
This course defines foundational terms such as cloud, data, and digital transformation. It also explores examples of companies around the world that are using cloud technology to revolutionize their businesses. The course provides an overview of the types of opportunities and challenges that companies often encounter in their digital transformation journey and aligns them with the Google Cloud solution pillars. But digital transformation isn’t just about using new technology. To truly transform, organizations also need to be innovative and scale an innovation mindset across the organization. The course offers best practices to help you achieve this.
This is the first course in the Cloud Digital Leader series. At the end of this course, enroll in the Innovating with Data and Google Cloud course.
-
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
