Welcome to our Q1 2020 roadmap. This is the content we plan to build over the next three months, between February 1 – and April 30, 2020. Let’s look at some of our roadmap highlights.
Atlassian Bamboo for CI/CD
We had a lot of requests for practical guides on how to apply DevOps toolchains at scale. So, we’re doing a learning path using Atlassian Bamboo for CI/CD. And in this learning path, we’ll help you learn how to use and manage Atlassian Bamboo for performing continuous integration and continuous deployment. We will provide you with not only the theory of how Atlassian Bamboo works, but also provide you with a detailed demonstration using a sample polyglot microservices-based project, which itself utilizes technologies such as Java, Dotnet and Go. Now this learning path will include several hands-on labs that we’ll train you to perform related to Atlassian Bamboo CI/CD tasks.
AliCloud has become a popular cloud choice with our enterprise customers and we have had a lot of requests for help getting teams up to speed with AliCloud services. I’m happy to announce we are building an AliCloud Fundamentals Learning Path this quarter.
Docker Certified Associate Exam Preparation
Next up, we’ve got Docker Certified Associate Exam Preparation — or the DCA. This learning path will help you learn and prepare for the Docker exam. It will provide you with detailed information covering each subject domain, so this learning path will include several hands-on labs that we’ll train you to perform tasks associated and assessed within the DCA exam.
Another big ask was for Kubernetes Tools, and the Docker Certified Associate Exam Preparation Learning Path will help you extend your existing Kubernetes skill set, by introducing you to several important and popular tools found within the Kubernetes tooling ecosystem.
We’ve got Helm, the package manager for Kubernetes. This course will provide you with detailed insights on how to use Helm to create and deploy packages into Kubernetes.
Next, we’ve got Istio, Kubernetes service mesh, and this course will provide you with detailed insights on how to use Istio to perform tasks such as to request routing, fault injections, retries, rate limits, traffic control, and traffic logging. Now the word “Istio” translates to “sale” in ancient Greek, but that has absolutely nothing to do with the roadmap. Anyway, let’s press on with this learning path. We also cover Knative, which allows us to run serverless applications on Kubernetes. And this course will provide you with detailed insights as to how to use Knative to deploy and operate serverless workloads into Kubernetes.
Azure Kubernetes Service
Next up, we’ve got AKS, or Azure Kubernetes Service, and this learning path will provide you with the basic concepts involved with running and operating the AKS-based cluster. Now AKS is Microsoft’s managed Kubernetes service and we will provide you with not only the theory of how AKS works but also detailed demonstrations, which take a sample polyglot microservice-based application and deploy it into a newly provisioned AKS cluster.
You’ve also asked for more site reliability in DevOps. So, we’re doing the SRE, DevOps Site Reliability Engineering certification provided by the DevOps Institute, and this learning path will include several courses following SRE principles in collaboration with the DevOps Institute, a fantastic learning path.
Artificial intelligence and machine learning
Another big ask was helping you leverage artificial intelligence and machine learning services. So, we’ve come up with an incredible curriculum of content, thanks to our relationship with QA.
First off, we’ve got data literacy. Now QA has developed a unique curriculum alongside Carolyn Carruthers, author of “The Chief Data Officer’s Playbook,” and this content will help senior stakeholders understand how to deliver transformation value from their data. This course will provide a ramp up for executives and business teams looking to get started in the wider organization.
Python, data science, and big data
We also include a learning path on practical big data analysis, and this includes an introduction to Python, data science, and big data, plus a deep introduction to the major big data technologies. This content will be ideal for people who are currently working as software engineers with data or in business intelligence and looking to level up to the next stage of large data analysis skills in contemporary patterns of data science.
Practical machine learning
We have a learning path practical machine learning and this is designed for people, who are already working on basic data science problems and starting the statistical analysis of data with Python. This learning path will help you recognize and explain what a machine learning approach looks like in an organization. So, you’ll learn how to implement different machine learning models, validate their quality, and how to implement them practically.
Introduction to statistics R Python analytics data science and machine learning
Another big request we had was practical data science with Python. This learning path sets up practitioners with working knowledge of the whole field of data science along with intermediate practical knowledge of key analytical tasks. It’s going to be ideal for fledgling data science practitioners or IT professionals, who wish to move to the exciting world of data analytics and machine learning.
Introduction to the R language
We’re also covering the fundamentals of R with an introduction to the R language. R language is a mathematical and statistical modeling language used extensively in data analysis and big data. It’s designed for anyone planning to work with large big data solutions and machine learning. By completing this learning path you will learn how to write programs using the R language, to create effective statistical outputs, and to visualize data using R’s library.
Data Science 101
We have a lot of data science content, so we’ve wrapped it in a data science 101 learning path. This learning path will provide a far start for anyone wanting to dive straight into data science. It will be made up of all of this new content plus existing content and it will be designed to fast-track you from newbie to ninja with data science.
Now that’s not to leave out all of our fantastic vendor-specific content. We’ve got some very exciting certification goodies in the plan for Q1. We’re doing the AZ-500, which is our Azure Security Technology Certification Learning Path. That will be completed this quarter. It’s in preview currently, but you’re able to get started with that.
In Q1, we’ll be adding a substantial amount of Azure DevOps content to get you started on preparing for Exam AZ-400: Microsoft Azure DevOps Solutions.
We’re covering the AI-102 exam which will be a preview version of our Azure AI solutions certification learning path. Now Microsoft has announced that it will be changing its four most popular Azure exams. AZ-103, AZ-203, AZ-300, and AZ-301. So, we will have learning paths ready to help you prepare for these new exams.
Google Cloud Platform
For Google Cloud Platform (GCP), we’re adding yet another GCP certification path for you — this time for the Professional Cloud Developer certification.
We’re redoing our AWS Solutions Architect – Associate Certification Preparation Learning Path to be absolutely in line with the latest curriculum. We will be releasing AWS Data Specialty Learning Path and the AWS Data Analytics Specialty Learning Path. Exciting stuff coming in all of these domains.
Online content roadmap
Last quarter, our team worked on building a better content roadmap. From now on, you will find a new roadmap managed by the Content team. It’s not fancy — it’s a live Google spreadsheet — but it is always updated and contains everything we are building. You can find our upcoming content on the Training Library menu.
I’m looking forward to seeing you in these learning paths and getting your feedback. As always, you are welcome to email us at firstname.lastname@example.org or feel free to drop your questions or comments below. Thank you very much.