Google has joined forces with Coursera to provide on-demand training to meet the cloud skills gap. The first course of of four-course specialization for Systems Operations Professionals starts today and so does a 1-week accelerated course introducing participants to the Big Data and Machine Learning capabilities of Google Cloud Platform.
Announcing the collaboration with Coursera, Louise Byrne, Head of Cloud Training Delivery for Google Cloud writes:
As more and more companies wish to take advantage of what cloud computing, data analytics and machine learning can do for their businesses, the gap between the knowledge needed to move to the cloud and the demand for such skills has grown enormously. Lack of expertise and cloud skills is often cited as the top challenge for companies wishing to migrate their business to the cloud.
Google Cloud Platform Fundamentals Starts March 6th 6-10 hours $59 This course introduces you to important concepts and terminology for working with Google Cloud Platform (GCP). You learn about, and compare, many of the computing and storage services available in Google Cloud Platform, including Google App Engine, Google Compute Engine, and Google Container Engine.
Computing, Storage and Security with Google Cloud Platform Starts March 13th 11-14 hrs $99 This course covers the details of Computing, Storage and Security on Google Cloud Platform (GCP). You learn how to manage both individual and groups of instances or virtual machines and their related resources like disks and IP addresses. You also learn about creating and configuring networks and firewall rules from scratch and about securely managing access to your projects using Identity and Access Management or IAM. Finally, you learn about managing Google Cloud Storage resources in your projects.
Configuration Management on Google Cloud Platform Starts March 13th 4-6 hours $99 In this course you learn how to create and configure Cloud SQL instances. You also learn how to work with Compute Engine metadata as well as startup and shutdown scripts.
Designing Highly Scalable Web Apps on Google Cloud Platform Starts March 13th 4-7 hrs $99 In this course you learn how to configure autoscaling with Compute Engine instance groups. You also configure a highly available architecture to serve web content across multiple fault-tolerant instance groups in different zones using a load balancer. This part of the course also includes an introductory lab on Google Cloud Deployment Manager to help you further automate the management and deployment of Google Cloud Platform resources. Finally, this course also includes a lab to walk you through the process of turning down and deleting the projects you used in this specialization. It’s important to work through this lab to avoid being charged for resources you no longer need.
While you can take the courses separately each subsequent ones relies on what you've already learned and done in the lab sessions - and while you can audit the course content for free if you want to access the labs you need to go for the paid option, which also entitles you to a course certificate if you pass all the graded assignments.
For all these courses and the one described below you'll need to sign up for the Google Cloud Platform free trial (currently blocked in China) for which you require a Google/Gmail account and a credit card or bank account.
Google Cloud Training is also launching an intermediate level and Coursera course forData analysts, Data scientists and Business analysts:
Provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities. At the end of this course, participants will be able to:
• Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform • Use CloudSQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform • Employ BigQuery and Cloud Datalab to carry out interactive data analysis • Choose between Cloud SQL, BigTable and Datastore • Train and use a neural network using TensorFlow
Participants are expected to have experience with one or more of the following:
• A common query language such as SQL • Extract, transform, load activities • Data modeling • Machine learning and/or statistics • Programming in Python