MLOps for Azure Machine Learning Training Course

About This Course

In this course, participants will acquire the skills to design and build services that retrieve data from both local and remote sources. Additionally, students will learn how to develop and deploy services within hybrid settings, encompassing on-premises servers and Microsoft Azure.

Audience Profile

Primary: .NET developers seeking to understand how to create services and deploy them to hybrid environments.
Secondary: .NET developers with experience in Web application development who are considering the development of 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 utilising the .NET platform.
• Describe the Microsoft Azure cloud platform, including its compute, data, and application hosting capabilities.
• Design and develop a data-driven application 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 on-premises and in Microsoft Azure.
• Deploy services to both on-premises and cloud environments and manage the interfaces and policies for these services.
• Select appropriate data storage solutions, and manage data caching, distribution, and synchronization.
• Monitor, log, and troubleshoot services.
• Explain claim-based identity concepts and standards, and implement authentication and authorisation with Azure Active Directory.
• Create scalable service applications.

This instructor-led, live training in Botswana (online or onsite) targets advanced AI engineers and data scientists with intermediate to advanced proficiency who aim to improve DeepSeek model performance, reduce latency, and implement AI solutions efficiently through contemporary MLOps practices.

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

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

Acquire the essential skills required to design an Azure AI solution by constructing a customer support chatbot using artificial intelligence on the Microsoft Azure platform. This includes leveraging language understanding and pre-built AI capabilities available within Azure Cognitive Services.

About This Course

This course introduces fundamental concepts relating to artificial intelligence (AI), alongside the services available in Microsoft Azure that can be leveraged to build AI solutions. The course is not designed to train students to become professional data scientists or software developers, but rather to build awareness of common AI workloads and the ability to identify Azure services that support them. The course is designed as a blended learning experience that combines instructor-led training with online materials on the Microsoft Learn platform (https://azure.com/learn). The practical exercises in the course are based on Learn modules, and students are encouraged to use the content on Learn as reference materials to reinforce what they learn in class and to explore topics in more depth.

Audience Profile

The Azure AI Fundamentals course is designed for anyone interested in learning about the types of solutions artificial intelligence (AI) makes possible, and the services on Microsoft Azure that you can use to create them. You do not need to have any prior experience using Microsoft Azure before taking this course, but a basic level of familiarity with computer technology and the Internet is assumed. Some of the concepts covered in the course require a basic understanding of mathematics, such as the ability to interpret charts. The course includes practical activities that involve working with data and running code, so a knowledge of fundamental programming principles will be helpful.

At Course Completion

After completing this course, you will be able to:

• Describe Artificial Intelligence workloads and considerations
• Describe fundamental principles of machine learning on Azure
• Describe features of computer vision workloads on Azure
• Describe features of Natural Language Processing (NLP) workloads on Azure
• Describe features of conversational AI workloads on Azure

This instructor-led, live training in Botswana (online or on-site) caters to intermediate to advanced professionals seeking to build and deploy AI-powered cloud applications using Microsoft Azure.

By the conclusion 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 course equips learners with the essential knowledge and competencies required to utilise PowerShell for the administration and automation of Windows server environments. Participants will develop the ability to identify and construct the necessary commands to execute specific tasks. Furthermore, students will acquire the skills to develop scripts that facilitate advanced operations, such as automating routine processes and producing reports. The content provides prerequisite capabilities 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 on any single product; however, Windows Server serves as the exemplar for teaching these techniques, as it functions as the common platform for all the aforementioned products.

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 a cloud environment. Azure Administrators will learn to provision, size, monitor, and adjust resources as required.

This course instructs Azure administrators on how to plan, deliver, and manage virtual desktop experiences and remote applications for any device on Azure. Through a combination of demonstrations and hands-on labs, students will learn to deploy virtual desktop experiences and apps on Azure Virtual Desktop, optimizing them for multi-session virtual environments.

This programme equips Solutions Architects with the skills to translate business requirements into secure, scalable, and reliable solutions. Key topics cover virtualisation, automation, networking, storage, identity, security, data platforms, and application infrastructure. Participants will explore how decisions in each of these areas impact the overall solution architecture.

Audience profile

This course is designed for IT Professionals who possess expertise in designing and implementing solutions on Microsoft Azure. Ideal candidates have broad knowledge of IT operations, encompassing networking, virtualisation, identity, security, business continuity, disaster recovery, data platforms, budgeting, and governance. While Azure Solution Architects primarily utilise the Azure Portal, they also employ the Command Line Interface as their proficiency grows. Candidates must demonstrate expert-level skills in Azure administration, along with practical experience in Azure development and DevOps processes.

This course equips Solutions Architects with the skills to translate business requirements into secure, scalable, and reliable solutions. Lessons cover design considerations related to logging, cost analysis, authentication and authorisation, governance, security, storage, high availability, and migration. In this role, professionals must make decisions across multiple areas that influence an overall design solution.

Docker serves as a containerisation 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 seeking to containerise and operationalise complete machine learning pipelines using Docker.

Upon completing this training, participants will be equipped to:

• Containerise machine learning training, validation, and inference workloads.
• Design and orchestrate end-to-end machine learning pipelines using Docker and complementary tools.
• Implement versioning, reproducibility, and continuous integration and delivery (CI/CD) for machine learning components.
• Deploy, monitor, and scale machine learning services within containerised environments.

Format of the Course

• Interactive lectures supported by practical demonstrations.
• Hands-on exercises focused on building real machine learning pipeline components.
• Live-lab implementation for end-to-end containerised workflows.

Course Customisation Options

• For customised training aligned with specific machine learning 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 Botswana (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 Botswana (online or onsite) is designed for engineers who wish to evaluate the approaches and tools currently available, helping them make informed decisions on the path forward for adopting MLOps within their organisation.

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 offers 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 who aim to build automated, scalable MLOps pipelines on Kubernetes.

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

• Design end-to-end CI/CD pipelines 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.