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 diverse sources. Learners will also acquire the skills to develop and deploy services across hybrid environments, covering both on-premises servers and Microsoft Azure.

Audience Profile

Primary: .NET developers looking to learn service development and deployment in hybrid environments.
Secondary: .NET developers with Web application development experience 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 within the .NET platform.
• Describe the Microsoft Azure cloud platform along with its compute, data, and application hosting offerings.
• Design and build a data-focused application using Visual Studio 2017 and Entity Framework Core.
• Design, implement, and consume HTTP services using ASP.NET Core.
• Extend HTTP services with ASP.NET Core.
• Host services in on-premises settings and within Microsoft Azure.
• Deploy services to on-premises and cloud environments, managing their interfaces and policies.
• Select appropriate data storage solutions, and manage caching, distribution, and synchronization of data.
• Monitor, log, and troubleshoot services.
• Explain claim-based identity concepts and standards, implementing authentication and authorization via Azure Active Directory.
• Create scalable service applications.

This instructor-led, live training in Nigeria (online or onsite) 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 expertise required to design Azure AI solutions by constructing a customer support chatbot powered by artificial intelligence on the Microsoft Azure platform. The curriculum covers language comprehension and the utilisation of pre-built AI capabilities within Azure Cognitive Services.

About This Course

This course provides an introduction to the core concepts of artificial intelligence (AI) and highlights the Microsoft Azure services available for developing AI solutions. It is not intended to train students to become professional data scientists or software developers; instead, it aims to raise awareness of typical AI workloads and equip learners with the ability to identify suitable Azure services for those tasks. Designed as a blended learning experience, the course combines instructor-led sessions with online resources on the Microsoft Learn platform (https://azure.com/learn). The practical exercises are derived from Learn modules, and students are encouraged to use Learn content as a reference to reinforce classroom learning and explore topics in greater depth.

Audience Profile

The Azure AI Fundamentals course is tailored for anyone eager to understand the types of solutions enabled by artificial intelligence (AI) and 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 expected. Some course concepts 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, knowledge of basic programming principles will be beneficial.

At Course Completion

Upon 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 Nigeria (available online or onsite) is designed for intermediate to advanced-level professionals who aspire 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 course equips students with the fundamental knowledge and skills needed to use PowerShell for administering and automating Windows server management. Learners will gain the ability to identify and construct the specific commands required to execute particular tasks. Additionally, students will learn to create scripts for advanced operations, such as automating repetitive processes and generating reports. This course provides prerequisite skills that support a wide range of Microsoft products, including Windows Server, Windows Client, Microsoft Azure, and Microsoft 365. While the course does not focus on any single product, it uses Windows Server as the primary example since it serves as the common platform for these technologies.

This programme 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 solutions.

Designed for Azure Administrators, this course 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 necessary.

This course instructs Azure administrators on how to plan, deliver, and manage virtual desktop experiences and remote applications for any device within Azure. Through a blend of demonstrations and hands-on lab exercises, students will learn to deploy and optimize virtual desktop environments and applications on Azure Virtual Desktop, ensuring efficient operation in multi-session virtual settings.

This course equips Solutions Architects with the skills to translate business requirements into secure, scalable, and reliable solutions. Key topics include virtualization, automation, networking, storage, identity management, security, data platforms, and application infrastructure. The curriculum outlines how decisions in each of these areas impact 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 knowledge of IT operations, including networking, virtualization, identity, security, business continuity, disaster recovery, data platforms, budgeting, and governance. Azure Solution Architects typically start with the Azure Portal and progressively utilize the Command Line Interface as their proficiency grows. Candidates must demonstrate expert-level skills in Azure administration, along with experience in Azure development processes and DevOps methodologies.

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

Docker is a containerization platform used to build reproducible, portable, and scalable environments for ML systems.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level technical professionals who wish to containerize and operationalize complete ML pipelines using Docker.

Upon completion of 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 supporting tools.
• Implement versioning, reproducibility, and CI/CD for ML components.
• Deploy, monitor, and scale ML services in containerized environments.

Format of the Course

• Interactive lectures supported by practical demonstrations.
• Hands-on exercises focused on building real 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 engineered to simplify the creation, training, and deployment of machine learning workloads on Kubernetes.

This instructor-led live training, available online or onsite, targets beginner to intermediate professionals eager to establish robust ML workflows via Kubeflow.

After completing this training, participants will acquire the competencies to:

• Traverse the Kubeflow ecosystem and its fundamental components.
• Create reproducible workflows utilizing Kubeflow Pipelines.
• Execute scalable training jobs on Kubernetes.
• Efficiently deploy machine learning models using Kubeflow Serving.

Course Format

• Structured presentations alongside collaborative discussions.
• Practical labs involving real Kubeflow components.
• Functional exercises designed to build comprehensive ML workflows.

Course Customization Options

• Tailored versions of this training can be arranged to suit your team’s specific technology stack and project needs.

This instructor-led, live training in Nigeria (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 Nigeria (online or onsite) is designed for engineers who wish to evaluate the available approaches and tools to make informed decisions on adopting MLOps within their organisations.

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 serves as 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 practitioners seeking to construct automated and scalable MLOps pipelines on Kubernetes.

Upon completing this training, participants will be able to:

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

Course Format

• Instructor-guided presentations combined with technical deep dives.
• Hands-on exercises focused on building real-world CI/CD workflows.
• Live-lab practice for deploying ML workloads to Kubernetes.

Course Customization Options

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