MLOps for Azure Machine Learning Training Course

About This Course

In this course, participants will acquire the skills to design and develop services that interact with local and remote data from diverse sources. Additionally, learners will master the process of deploying services to hybrid environments, encompassing both on-premises servers and Microsoft Azure.

Audience Profile

Primary: .NET developers seeking to learn how to create services and deploy them within hybrid environments.
Secondary: .NET developers with prior web application development experience who are interested in building new applications or migrating existing ones to Microsoft Azure.

At Course Completion

Upon finishing this course, students will be equipped 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 offerings.
• Design and develop data-centric applications utilizing Visual Studio 2017 and Entity Framework Core.
• Design, implement, and consume HTTP services using ASP.NET Core.
• Extend HTTP services via ASP.NET Core.
• Host services both on-premises and within Microsoft Azure.
• Deploy services to on-premises and cloud environments, and manage their service interfaces and policies.
• Select appropriate data storage solutions, and manage data caching, distribution, and synchronization.
• Monitor, log, and troubleshoot services.
• 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 Kenya (online or on-site) is designed for advanced AI engineers and data scientists with intermediate-to-advanced experience who aim to improve DeepSeek model performance, reduce latency, and efficiently deploy AI solutions using contemporary MLOps practices.

By the conclusion 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 required to design Azure AI solutions by constructing a customer support chatbot leveraging artificial intelligence on the Microsoft Azure platform. This includes implementing language understanding capabilities and utilising pre-built AI functionalities available through 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. The program is not intended to train students to become professional data scientists or software developers; instead, it aims to build awareness of common AI workloads and the ability to identify appropriate Azure services to support them. Designed as a blended learning experience, the course combines instructor-led training with online materials on the Microsoft Learn platform (https://azure.com/learn). The hands-on exercises are based on 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 designed for anyone interested in understanding the types of solutions that artificial intelligence (AI) enables, 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 basic understanding of mathematics, such as the ability to interpret charts. The course includes hands-on activities involving data work and code execution, 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 live, instructor-led training in Kenya (online or onsite) is designed for intermediate to advanced professionals aiming 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 learners with the essential knowledge and skills required to administer and automate Windows server environments using PowerShell. Participants will develop the ability to identify and construct the necessary commands to execute specific tasks. Furthermore, students will learn to build scripts for advanced operations, such as automating routine activities and generating reports. The course provides prerequisite skills applicable to a wide array of Microsoft products, including Windows Server, Windows Client, Microsoft Azure, and Microsoft 365. Aligning with this objective, the course does not concentrate on a single product; however, Windows Server serves as the primary example platform for the techniques taught, as it is the common foundation for these 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 sites, manage network traffic, implement storage solutions, create and scale virtual machines, deploy web apps and containers, back up and share data, and monitor your solutions.

This course is designed for Azure Administrators. The Azure Administrator manages, implements, and monitors identity, governance, storage, compute, and virtual networks within a cloud environment. The Azure Administrator provisions, sizes, monitors, and adjusts resources as necessary.

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 labs, students will deploy virtual desktop environments and applications on Azure Virtual Desktop, learning to optimize them for multi-session virtual environments.

This course equips Solutions Architects with the skills to translate business requirements into secure, scalable, and reliable cloud solutions. Key topics include virtualization, automation, networking, storage, identity management, security, data platforms, and application infrastructure. The curriculum explains 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, covering networking, virtualization, identity, security, business continuity, disaster recovery, data platforms, budgeting, and governance. Azure Solution Architects utilize the Azure Portal and, as their proficiency grows, the Command Line Interface. Candidates must demonstrate expert-level skills in Azure administration, along with experience in Azure development and DevOps processes.

This course equips Solutions Architects with the skills to transform business requirements into secure, scalable, and reliable technical solutions. The curriculum covers critical design considerations such as logging, cost management, authentication, authorization, governance, security, storage, high availability, and migration strategies. Professionals in this role must make informed decisions across multiple domains to shape an effective overall design.

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 aiming 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 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 construction, training, and deployment of machine learning workloads on Kubernetes.

This instructor-led, live training (available online or on-site) is tailored for beginner to intermediate-level professionals aiming to establish reliable ML workflows using Kubeflow.

Upon completing this training, participants will acquire the following skills:

• Navigating the Kubeflow ecosystem and its core components.
• Creating reproducible workflows via Kubeflow Pipelines.
• Executing scalable training jobs on Kubernetes.
• Efficiently serving machine learning models using Kubeflow Serving.

Course Format

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

Course Customization Options

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

This instructor-led live training in Kenya (online or onsite) targets 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-premises and in the cloud.
• Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
• Run complete machine learning pipelines across diverse architectures and cloud environments.
• Use Kubeflow to create and manage Jupyter notebooks.
• Develop ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led, live training in Kenya (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 practitioners aiming to build automated, scalable MLOps pipelines on Kubernetes.

Upon completing 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 to 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.