OpenFaas for Developers Training Course

About This Course

In this course, students will gain the skills needed to design and develop services that can access local and remote data from a variety of sources. They will also learn how to develop and deploy these services to hybrid environments, which include both on-premises servers and Microsoft Azure.

Audience Profile

Primary: .NET developers who are interested in learning how to create and deploy services to hybrid environments.
Secondary: .NET developers with experience in web application development who are considering developing new applications or migrating existing ones to Microsoft Azure.

At Course Completion

Upon completing this course, students will be able to:

• Explain the fundamental concepts of service development and data access strategies using the .NET platform.
• Understand the Microsoft Azure cloud platform and its offerings for compute, data, and application hosting.
• Design and develop a data-centric application using Visual Studio 2017 and Entity Framework Core.
• Create and consume HTTP services with ASP.NET Core.
• Enhance HTTP services using ASP.NET Core.
• Host services both on-premises and in Microsoft Azure.
• Deploy services to both on-premises and cloud environments, and manage the interface and policies for these services.
• Select a data storage solution, implement caching, distribute, and synchronize data.
• Monitor, log, and troubleshoot services effectively.
• Understand claim-based identity concepts and standards, and implement authentication and authorization using Azure Active Directory.
• Create scalable service applications.

Acquire the essential skills for designing an Azure AI solution by creating a customer support chatbot that leverages artificial intelligence from the Microsoft Azure platform. This includes utilizing language understanding and pre-built AI capabilities provided by Azure Cognitive Services.

About This Course

This course provides an introduction to the foundational concepts related to artificial intelligence (AI) and the services available in Microsoft Azure that can be utilized to create AI solutions. The aim is not to turn students into professional data scientists or software developers, but rather to build their awareness of common AI workloads and enable them to identify the appropriate Azure services to support these tasks. The course combines instructor-led sessions with online materials from the Microsoft Learn platform (https://azure.com/learn). Practical exercises are based on modules from Learn, and students are encouraged to use this content as reference material to reinforce their learning and delve deeper into specific topics.

Audience Profile

The Azure AI Fundamentals course is tailored for anyone interested in understanding the types of solutions that artificial intelligence (AI) can facilitate, and the services on Microsoft Azure that can be used to develop them. No prior experience with Microsoft Azure is required, but a basic familiarity with computer technology and the Internet is assumed. Some concepts covered in the course require a basic understanding of mathematics, such as interpreting charts. The hands-on activities involve working with data and running code, so having a foundational knowledge of programming principles will be beneficial.

At Course Completion

Upon completing this course, you will be able to:

• Describe the workloads and considerations involved in Artificial Intelligence.
• Explain the fundamental principles of machine learning on Azure.
• Outline the features of computer vision workloads on Azure.
• Detail the capabilities of Natural Language Processing (NLP) workloads on Azure.
• Describe the features of conversational AI workloads on Azure.

This instructor-led, live training in Slovakia (online or onsite) is aimed at intermediate-level to advanced-level professionals who wish to build and deploy AI-powered cloud applications using Microsoft Azure.

By the end of this training, participants will be able to:

• Develop event-driven and serverless applications using Azure Functions.
• Manage Azure storage solutions and virtual machines.
• Deploy and scale web applications using Azure App Service and Docker containers.
• Integrate AI, machine learning, and natural language processing using Azure AI Services.
• Leverage GitHub Copilot to assist in AI-driven cloud application development.

This instructor-led, live training in Slovakia (online or onsite) is aimed at engineers who wish to use Azure ML's drag-and-drop platform to deploy Machine Learning workloads without having to purchase software and hardware and without having to worry about maintenance and deployment.

By the end of this training, participants will be able to:

• Write highly-accurate machine learning models using Python, R, or zero-code tools.
• Leverage Azure's available data sets and algorithms to train and track machine learning and deep-learning models.
• Use Azures interactive workspace to collaboratively develop ML models.
• Choose from different Azure-supported ML frameworks such as PyTorch, TensorFlow, and scikit-learn.

This instructor-led, live training in Slovakia (onsite or remote) is aimed at engineers who wish to deploy and manage IoT devices on AWS.

By the end of this training, participants will be able to build an IoT platform that includes the deployment and management of a backend, gateway, and devices on top of AWS.

This instructor-led, live training in Slovakia (online or onsite) is aimed at developers who wish to install, configure, and manage AWS IoT Greengrass capabilities to create applications for various devices.

By the end of this training, participants will be able to use AWS IoT Greengrass to build, deploy, manage, secure, and monitor applications on intelligent devices.

This instructor-led, live training in Slovakia (onsite or remote) is aimed at developers who wish to use AWS Lambda to build and deploy services and applications to the cloud, without needing to worry about provisioning the execution environment (servers, VMs and containers, availability, scalability, storage, etc.).

By the end of this training, participants will be able to:

• Configure AWS Lambda to execute a function.
• Understand FaaS (Functions as a Service) and the advantages of serverless development.
• Build, upload and execute AWS Lambda functions.
• Integrate Lambda functions with different event sources.
• Package, deploy, monitor and troubleshoot Lambda based applications.

DO180 is an introductory course that covers containers, Kubernetes fundamentals, and Red Hat OpenShift platform concepts, with a strong emphasis on hands-on skills.

This instructor-led, live training (available both online and onsite) is designed for technical professionals at beginner to intermediate levels who are interested in learning container workflows, Kubernetes basics, and how to deploy and operate applications on OpenShift.

By the end of this training, participants will be able to:

• Create and manage container images and registries with best practices for reproducibility and security.
• Deploy and manage Kubernetes objects like pods, deployments, and services in OpenShift.
• Leverage OpenShift features such as routes, build configurations, and the web console to streamline application delivery.
• Implement persistent storage, configuration management, and secret handling for stateful workloads.
• Apply basic security measures, role-based access control (RBAC), and monitoring practices to ensure healthy clusters and applications.

Format of the Course

• Interactive lectures and discussions.
• Hands-on labs in a live OpenShift environment each day.
• Scenario-based exercises and troubleshooting workshops.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in Slovakia (online or onsite) is aimed at advanced-level professionals who wish to deepen their understanding of DevOps practices and streamline development processes using AWS Cloud9.

By the end of this training, participants will be able to:

• Set up and configure AWS Cloud9 for DevOps workflows.
• Implement continuous integration and continuous delivery (CI/CD) pipelines.
• Automate testing, monitoring, and deployment processes using AWS Cloud9.
• Integrate AWS services such as Lambda, EC2, and S3 into DevOps workflows.
• Utilize source control systems like GitHub or GitLab within AWS Cloud9.

This instructor-led, live training in Slovakia (online or onsite) is aimed at intermediate-level professionals who wish to learn how to effectively build, deploy, and maintain serverless applications on AWS Cloud9 and AWS Lambda.

By the end of this training, participants will be able to:

• Understand the fundamentals of serverless architecture.
• Set up AWS Cloud9 for serverless application development.
• Develop, test, and deploy serverless applications using AWS Lambda.
• Integrate AWS Lambda with other AWS services such as API Gateway and S3.
• Optimize serverless applications for performance and cost efficiency.

This instructor-led, live training in Slovakia (online or onsite) is aimed at engineers who wish to use Helm to streamline the process of installing and managing Kubernetes applications.

By the end of this training, participants will be able to:

• Install and configure Helm.
• Create reproducible builds of Kubernetes applications.
• Share applications as Helm charts.
• Run third-party applications saved as Helm charts.
• Manage releases of Helm packages.

Summary:

• Fundamentals of IoT architecture and functionality
• Understanding "Things", "Sensors", the Internet, and their alignment with business functions in IoT
• Key components of all IoT software—hardware, firmware, middleware, cloud, and mobile applications
• IoT functionalities such as fleet management, data visualization, SaaS-based fleet management and data visualization, alerts/alarms, sensor onboarding, device onboarding, and geo-fencing
• Basics of IoT device communication with the cloud using MQTT
• Connecting IoT devices to AWS via MQTT (AWS IoT Core)
• Linking AWS IoT Core with AWS Lambda for computation and data storage using DynamoDB
• Integrating a Raspberry PI with AWS IoT Core for simple data communication
• Practical exercises with Raspberry PI and AWS IoT Core to create a smart device
• Data visualization from sensors and communication through a web interface

This instructor-led, live training in Slovakia (online or onsite) is aimed at beginner-level to intermediate-level software developers and DevOps professionals who wish to learn how to set up and manage a local Kubernetes environment using Minikube.

By the end of this training, participants will be able to:

• Install and configure Minikube on their local machine.
• Understand the basic concepts and architecture of Kubernetes.
• Deploy and manage containers using kubectl and the Minikube dashboard.
• Set up persistent storage and networking solutions for Kubernetes.
• Utilize Minikube for developing, testing, and debugging applications.

This instructor-led, live training in Slovakia (online or onsite) is aimed at intermediate-level developers and DevOps engineers who wish to use Minikube as a part of their development workflow.

By the end of this training, participants will be able to:

• Set up and manage a local Kubernetes environment using Minikube.
• Understand how to deploy, manage, and debug applications on Minikube.
• Integrate Minikube into their continuous integration and deployment pipelines.
• Optimize their development process using Minikube's advanced features.
• Apply best practices for local Kubernetes development.