MLOps for Azure Machine Learning Training Course

About This Course

This course teaches students how to design and build services that retrieve local and remote data from a variety of sources. Participants will also learn how to develop and deploy these services within hybrid environments, encompassing both on-premises servers and Microsoft Azure.

Audience Profile

Primary: .NET developers aiming to learn how to develop services and deploy them to hybrid environments.
Secondary: .NET developers with experience in Web application development who are interested in creating new applications or migrating existing ones to Microsoft Azure.

At Course Completion

Upon finishing this course, students will be able to:

• Explain the fundamental concepts of service development and data access strategies using the .NET platform.
• Describe the Microsoft Azure cloud platform along with 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 using ASP.NET Core.
• Host services both on-premises and in Microsoft Azure.
• Deploy services to on-premises and cloud environments, managing their interfaces and policies.
• Select appropriate data storage solutions, cache, distribute, and synchronize data.
• Monitor, log, and troubleshoot services.
• Explain claim-based identity concepts and standards, and implement authentication and authorization with Azure Active Directory.
• Create scalable service applications.

This instructor-led, live training in Serbia (online or onsite) targets advanced-level AI engineers and data scientists with intermediate-to-advanced experience who aim to enhance DeepSeek model performance, reduce latency, and deploy AI solutions efficiently using modern MLOps practices.

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

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

Acquire the essential expertise required to design Azure AI solutions. You will gain hands-on experience building a customer support chatbot powered by artificial intelligence via the Microsoft Azure platform, leveraging language understanding capabilities and pre-built AI features within Azure Cognitive Services.

About This Course

This course provides an introduction to the core concepts of artificial intelligence (AI) and the Microsoft Azure services available for building AI solutions. Rather than aiming to train students to become professional data scientists or software developers, the course focuses on raising awareness of common AI workloads and helping participants 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 hands-on exercises are aligned with Learn modules, and students are encouraged to use the Learn platform as a reference to reinforce 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 made possible by artificial intelligence (AI) and the Microsoft Azure services available to create them. No prior experience with Microsoft Azure is required, though a basic familiarity with computer technology and the Internet is assumed. Some concepts covered require a fundamental understanding of mathematics, particularly the ability to interpret charts. Additionally, the course includes hands-on activities involving data manipulation and code execution, so knowledge of basic programming principles will be beneficial.

At Course Completion

After completing this course, you will be able to:

• Describe AI workloads and their associated considerations
• Explain fundamental principles of machine learning on Azure
• Outline the features of computer vision workloads on Azure
• Outline the features of Natural Language Processing (NLP) workloads on Azure
• Outline the features of conversational AI workloads on Azure

This instructor-led, live training in Serbia (available online or onsite) is designed for intermediate to advanced professionals who wish to construct and deploy AI-powered cloud applications using Microsoft Azure.

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

• Create 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 via Azure AI Services.
• Leverage GitHub Copilot to aid in AI-driven cloud application development.

This course equips students with the essential knowledge and skills needed to utilize PowerShell for administering and automating the management of Windows servers. Learners will develop the ability to identify and construct the specific commands required to execute particular tasks. Furthermore, students will learn how to write scripts to handle advanced operations, such as automating repetitive processes and generating reports. The course provides prerequisite skills that support a wide array of Microsoft products, including Windows Server, Windows Client, Microsoft Azure, and Microsoft 365. Consistent with this objective, the course does not focus on any single product; however, Windows Server, being the common platform for all these technologies, serves as the primary example for teaching these techniques.

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

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

This course equips Azure administrators with the skills to plan, deliver, and manage virtual desktop experiences and remote applications for any device on Azure. Through a combination of demonstrations and hands-on lab exercises, students will learn to deploy and optimize virtual desktop experiences and applications on Azure Virtual Desktop within multi-session virtual environments.

This program equips Solutions Architects with the skills to translate business requirements into secure, scalable, and reliable solutions. Key topics include virtualization, automation, networking, storage, identity, security, data platforms, and application infrastructure. The course highlights how decisions in each of these areas influence the overall solution architecture.

Audience profile

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

This course equips Solutions Architects with the skills to translate business requirements into secure, scalable, and reliable solutions. Key topics include design considerations for logging, cost analysis, authentication and authorization, governance, security, storage, high availability, and migration. This role involves making critical decisions across multiple areas that influence the overall design solution.

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

This instructor-led, live training (available online or onsite) targets intermediate to advanced technical professionals who aim to containerize and operationalize complete ML pipelines using Docker.

Upon completing this training, participants will be able to:

• Containerize ML training, validation, and inference workloads.
• Design and orchestrate end-to-end ML pipelines using Docker and complementary tools.
• Implement versioning, reproducibility, and CI/CD practices for ML components.
• Deploy, monitor, and scale ML services within containerized environments.

Format of the Course

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

Course Customization Options

• For customized training aligned with specific ML infrastructure needs, please contact us to discuss 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 Serbia (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.
• Use Kubeflow to spawn and manage Jupyter notebooks.
• Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led, live training in Serbia (online or onsite) is aimed at engineers who wish to evaluate the approaches and tools available today to make an intelligent decision on the path forward in adopting MLOps within their organization.

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

• Install and configure various MLOps frameworks and tools.
• Assemble the right kind of team with the right 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's reproduceable 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 either online or onsite, is designed for intermediate-level professionals seeking to construct automated, scalable MLOps pipelines on Kubernetes.

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

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

Course Format

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

Course Customization Options

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