Architecting Microsoft Azure Solutions Training Course

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 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 course equips IT Professionals with the skills to manage their Azure subscriptions, secure identities, administer infrastructure, configure virtual networking, connect Azure with on-premises sites, manage network traffic, implement storage solutions, create and scale virtual machines, deploy web apps and containers, back up and share data, and monitor the overall solution.

This course is designed for Azure Administrators. The role of an Azure Administrator involves implementing, managing, and monitoring identity, governance, storage, compute, and virtual networks in a cloud environment. Azure Administrators are responsible for provisioning, sizing, monitoring, and adjusting resources as needed to ensure optimal performance.

This course equips Solutions Architects with the skills to transform business requirements into secure, scalable, and reliable solutions. The curriculum covers essential topics such as virtualization, automation, networking, storage, identity management, security, data platforms, and application infrastructure. It also highlights how decisions in each of these areas impact the overall solution.

Audience Profile

This course is designed for IT Professionals who have extensive experience in designing and implementing solutions on Microsoft Azure. Participants should possess a broad understanding of IT operations, encompassing networking, virtualization, identity management, security, business continuity, disaster recovery, data platforms, budgeting, and governance. Azure Solution Architects typically start by using the Azure Portal and gradually transition to utilizing the Command Line Interface as they become more proficient. Candidates are expected to have expert-level skills in Azure administration and hands-on experience with Azure development processes and DevOps practices.

Microsoft Azure Infrastructure and Deployment

This instructor-led, live training in Slovakia (online or onsite) is aimed at DevOps engineers, developers, and project managers who wish to utilize Azure DevOps to build and deploy optimized enterprise applications faster than traditional development approaches.

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

• Understand the fundamental DevOps vocabulary and principles.
• Install and configure the necessary Azure DevOps tools for software development.
• Utilize Azure DevOps tools and services to continuously adapt to the market.
• Build enterprise applications and evaluate current development processes upon Azure DevOps solutions.
• Manage teams more efficiently and accelerate software deployment time.
• Adopt DevOps development practices within the organization.

This instructor-led, live training in Slovakia (online or onsite) is aimed at data scientists who wish to use Azure Machine Learning to build end-to-end machine learning models for predictive analysis.

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

• Build machine learning models with zero programming experience.
• Create predictive algorithms with Azure Machine Learning.
• Deploy production ready machine learning algorithms.

This instructor-led, live training in Slovakia (online or onsite) is aimed at security administrators who wish to secure Azure workloads.

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

• Administrate host security, network security, and more.
• Set up storage and database security in Azure.
• Implement security monitoring using Azure resources.
• Prevent malicious cyber attacks on data and infrastructures.

This instructor-led, live training in Slovakia (online or onsite) is aimed at developers who wish to learn how to build microservices on Microsoft Azure Service Fabric (ASF).

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

• Use ASF as a platform for building and managing microservices.
• Understand key microservices programming concepts and models.
• Create a cluster in Azure.
• Deploy microservices on premises or in the cloud.
• Debug and troubleshoot a live microservice application.

The Azure Bot Service integrates the capabilities of the Microsoft Bot Framework and Azure Functions to facilitate the rapid development of intelligent bots.

In this instructor-led, live training, participants will learn how to efficiently create an intelligent bot using Microsoft Azure.

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

• Understand the basics of intelligent bots
• Learn how to develop intelligent bots using cloud applications
• Gain insight into utilizing the Microsoft Bot Framework, the Bot Builder SDK, and the Azure Bot Service
• Discover how to design bots using bot patterns
• Create their first intelligent bot using Microsoft Azure

Audience

• Developers
• Hobbyists
• Engineers
• IT Professionals

Format of the course

• Part lecture, part discussion, exercises, and extensive hands-on practice

In this instructor-led, live training in Slovakia (onsite or remote) participants will learn the fundamental concepts, components, and services of Microsoft Azure as they step through the creation of a sample cloud application.

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

• Understand the basics of Microsoft Azure
• Understand the different Azure tools and services
• Learn how to use Azure for building cloud applications

The Internet of Things (IoT) is a network infrastructure that connects physical devices and software applications wirelessly, enabling them to communicate and exchange data through network communications, cloud computing, and data capture. Azure offers a comprehensive suite of cloud services, including an IoT Suite with preconfigured solutions designed to help developers expedite the development of IoT projects.

In this instructor-led, live training, participants will learn how to develop IoT applications using Azure.

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

• Grasp the fundamental concepts of IoT architecture
• Install and configure the Azure IoT Suite
• Understand the advantages of using Azure for programming IoT systems
• Implement various Azure IoT services such as IoT Hub, Functions, Stream Analytics, Power BI, Cosmos DB, DocumentDB, and IoT Device Management
• Build, test, deploy, and troubleshoot an IoT system using Azure

Audience

• Developers
• Engineers

Format of the course

• Part lecture, part discussion, exercises, and extensive hands-on practice

Note

• 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 engineers who wish to deploy Machine Learning workloads to Azure cloud.

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

• Install and configure Kubernetes, Kubeflow and other needed software on Azure.
• Use Azure Kubernetes Service (AKS) to simplify the work of initializing a Kubernetes cluster on Azure.
• Create and deploy a Kubernetes pipeline for automating and managing ML models in production.
• Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.
• Leverage other AWS managed services to extend an ML application.

In this instructor-led, live training in Slovakia (online or onsite), participants will learn how to set up and manage a production-scale container environment using Kubernetes on AKS. 

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

• Configure and manage Kubernetes on AKS.
• Deploy, manage and scale a Kubernetes cluster.
• Deploy containerized (Docker) applications on Azure.
• Migrate an existing Kubernetes environment from on-premise to AKS cloud.
• Integrate Kubernetes with third-party continuous integration (CI) software.
• Ensure high availability and disaster recovery in Kubernetes.

This instructor-led, live training in (online or onsite) is aimed at machine learning engineers who wish to use Azure Machine Learning and Azure DevOps to facilitate MLOps practices.

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

• Build reproducible workflows and machine learning models.
• Manage the machine learning lifecycle.
• Track and report model version history, assets, and more.
• Deploy production ready machine learning models anywhere.