MLOps for Azure Machine Learning Training Course

About This Course

Through this course, learners will acquire the skills to design and build services that retrieve local and remote data from diverse sources. Additionally, participants will learn how to develop and deploy these services within hybrid environments, spanning on-premises servers and Microsoft Azure.

Audience Profile

Primary audience: .NET developers seeking to learn service development and deployment to hybrid environments.
Secondary audience: .NET developers with experience in web application development who are interested in building new applications or migrating existing ones to Microsoft Azure.

At Course Completion

Upon finishing this course, students will be able to:

• Explain fundamental concepts of service development and data access strategies within the .NET platform.
• Describe the Microsoft Azure cloud platform, including its compute, data, and application hosting capabilities.
• Design and develop data-centric applications using Visual Studio 2017 and Entity Framework Core.
• Design, implement, and consume HTTP services using ASP.NET Core.
• Extend HTTP services through ASP.NET Core.
• Host services both on-premises and in Microsoft Azure.
• Deploy services to on-premises and cloud environments while managing their interfaces and policies.
• Select appropriate data storage solutions and manage caching, distribution, and synchronization of data.
• Monitor, log, and troubleshoot services effectively.
• Describe claim-based identity concepts and standards, and implement authentication and authorization using Azure Active Directory.
• Create scalable service applications.

This instructor-led, live training in Slovakia (online or on-site) is aimed at advanced-level AI engineers and data scientists with intermediate-to-advanced experience who wish to enhance DeepSeek model performance, minimize latency, and deploy AI solutions efficiently using modern MLOps practices.

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

• Optimize DeepSeek models for efficiency, accuracy, and scalability.
• Implement best practices for MLOps and model versioning.
• Deploy DeepSeek models on cloud and on-premise infrastructure.
• Monitor, maintain, and scale AI solutions effectively.

Acquire the essential skills to design Azure AI solutions by constructing a customer support chatbot that leverages artificial intelligence from the Microsoft Azure platform. This includes implementing language understanding and utilizing pre-built AI capabilities provided by Azure Cognitive Services.

About This Course

This course provides an overview of fundamental concepts related to artificial intelligence (AI) and the Microsoft Azure services available for developing AI solutions. Rather than aiming to train students to become professional data scientists or software developers, the course focuses on building awareness of common AI workloads and enabling participants to identify the appropriate Azure services to support those workloads. Designed as a blended learning experience, it combines instructor-led training with online materials hosted on the Microsoft Learn platform (https://azure.com/learn). The course includes hands-on exercises based on Learn modules, and students are encouraged to use Learn content as a reference to reinforce their classroom learning and explore topics in greater depth.

Audience Profile

The Azure AI Fundamentals course is designed for individuals interested in understanding the types of solutions that artificial intelligence (AI) enables, as well as the Microsoft Azure services used to build them. Prior experience with Microsoft Azure is not required, but a basic familiarity with computer technology and the Internet is assumed. Some concepts covered require a fundamental understanding of mathematics, such as the ability to interpret charts. Since the course includes hands-on activities involving data manipulation and code execution, a foundational knowledge of programming principles will be beneficial.

At Course Completion

After completing this course, you will be able to:

• Describe AI workloads and key considerations
• Describe the fundamental principles of machine learning on Azure
• Describe the features of computer vision workloads on Azure
• Describe the features 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 tailored for intermediate to advanced professionals seeking to build and deploy AI-enhanced cloud applications on Microsoft Azure.

Upon completion of this training, participants will be capable of:

• Developing event-driven and serverless applications using Azure Functions.
• Managing Azure storage resources and virtual machines.
• Deploying and scaling web applications via Azure App Service and Docker containers.
• Integrating artificial intelligence, machine learning, and natural language processing capabilities through Azure AI Services.
• Utilizing GitHub Copilot to support AI-driven cloud application development.

This course equips students with the essential knowledge and skills required to utilize PowerShell for the administration and automation of Windows server environments. Participants will develop the ability to identify and construct the specific commands needed to execute designated tasks. Furthermore, students will learn how to create scripts to handle advanced responsibilities, such as automating repetitive processes and generating detailed reports. This course delivers prerequisite competencies that support a wide array of Microsoft products, including Windows Server, Windows Client, Microsoft Azure, and Microsoft 365. Aligned with this objective, the course does not concentrate exclusively on any single product; however, Windows Server serves as the primary example platform for demonstrating the techniques taught, as it underpins all of these Microsoft solutions.

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

Designed for Azure Administrators, this training covers the implementation, management, and monitoring of identity, governance, storage, compute, and virtual networks within cloud environments. Azure Administrators will learn to provision, size, monitor, and adjust resources as needed.

This course equips Azure administrators with the skills to plan, deliver, and manage virtual desktop experiences and remote applications for any device on Azure. Participants will engage in a combination of demonstrations and hands-on labs to deploy virtual desktops and applications on Azure Virtual Desktop, optimizing them for multi-session virtual environments.

This course equips Solutions Architects with the skills to translate business requirements into secure, scalable, and reliable solutions. The curriculum covers virtualization, automation, networking, storage, identity, security, data platforms, and application infrastructure, outlining how decisions in each of these areas impact the overall solution.

Audience profile

This course is designed for IT Professionals who specialize in designing and implementing solutions on Microsoft Azure. Participants should possess broad knowledge of IT operations, including networking, virtualization, identity, security, business continuity, disaster recovery, data platforms, budgeting, and governance. Azure Solution Architects utilize the Azure Portal and, as they become more proficient, the Command Line Interface. Candidates must demonstrate expert-level Azure administration skills along with experience in Azure development and DevOps processes.

This course equips Solutions Architects with the skills to translate business requirements into secure, scalable, and reliable Azure solutions. Key lessons cover design considerations for logging, cost analysis, authentication and authorization, governance, security, storage, high availability, and migration. Professionals in this role must make strategic decisions across multiple domains to shape the overall solution architecture.

Docker serves as a containerization platform designed to create reproducible, portable, and scalable environments for machine learning systems.

This instructor-led training, available both online and onsite, targets intermediate to advanced technical professionals aiming to containerize and operationalize complete ML pipelines using Docker.

After completing this training, participants will be capable of:

• Containerizing ML workloads for training, validation, and inference.
• Designing and orchestrating end-to-end ML pipelines with Docker and related tools.
• Implementing versioning, reproducibility, and CI/CD processes for ML components.
• Deploying, monitoring, and scaling ML services within containerized environments.

Course Format

• Interactive lectures complemented by practical demonstrations.
• Hands-on exercises centered on constructing real-world ML pipeline components.
• Live-lab implementation for end-to-end containerized workflows.

Course Customization Options

Kubeflow is an open-source platform designed to streamline building, training, and deploying machine learning workloads on Kubernetes.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level professionals who wish to build reliable ML workflows using Kubeflow.

Upon completion of this training, attendees will gain the skills to:

• Navigate the Kubeflow ecosystem and core components.
• Build reproducible workflows with Kubeflow Pipelines.
• Run scalable training jobs on Kubernetes.
• Serve machine learning models efficiently using Kubeflow Serving.

Format of the Course

• Guided presentations and collaborative discussions.
• Hands-on labs with real Kubeflow components.
• Practical exercises to build end-to-end ML workflows.

Course Customization Options

• Customized versions of this training can be arranged to align with your team’s technology stack and project requirements.

This instructor-led live training in Slovakia (online or onsite) is aimed at developers and data scientists who wish to build, deploy, and manage machine learning workflows on Kubernetes.

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

• Install and configure Kubeflow on premise and in the cloud.
• Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
• Run entire machine learning pipelines on diverse architectures and cloud environments.
• Using Kubeflow to spawn and manage Jupyter notebooks.
• Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led live training in Slovakia (online or onsite) targets engineers who wish to evaluate current approaches and tools. This evaluation supports intelligent decision-making regarding the adoption of MLOps within their organization.

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

• Install and configure various MLOps frameworks and tools.
• Assemble a team with the appropriate skills for constructing and supporting an MLOps system.
• Prepare, validate, and version data for use by ML models.
• Understand the components of an ML Pipeline and the tools needed to build one.
• Experiment with different machine learning frameworks and servers for deploying to production.
• Operationalize the entire Machine Learning process so that it is reproducible and maintainable.

MLOps on Kubernetes provides a framework for automating the training, validation, packaging, and deployment of machine learning models through containerized pipelines and GitOps workflows.

This instructor-led live training, available online or onsite, is designed for intermediate-level practitioners looking to build automated and scalable MLOps pipelines on Kubernetes.

Upon completion of this training, participants will be able to:

• Design end-to-end CI/CD pipelines tailored for machine learning.
• Implement GitOps workflows for model deployment and versioning.
• Automate the training, testing, and packaging of ML models.
• Integrate monitoring, alerting, and rollback strategies.

Course Format

• Instructor-guided presentations and technical deep dives.
• Hands-on exercises that build real-world CI/CD workflows.
• Live-lab practice deploying ML workloads to Kubernetes.

Course Customization Options

• Organizations may request tailored content aligned with their internal MLOps tools and infrastructure.