MLOps for Azure Machine Learning Training Course

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

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

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

This instructor-led, live training in South Africa (online or onsite) is tailored for intermediate to advanced professionals who aim to build and deploy AI-powered cloud applications using Microsoft Azure.

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

• Create event-driven and serverless applications using Azure Functions.
• Administer Azure storage solutions and virtual machines.
• Deploy and scale web applications utilizing Azure App Service and Docker containers.
• Integrate AI, machine learning, and natural language processing capabilities via Azure AI Services.
• Harness GitHub Copilot to support AI-driven cloud application development.

This course equips students with the essential knowledge and skills required to utilise PowerShell for the administration and automation of Windows server environments. Learners will acquire the ability to identify and construct the necessary commands to execute specific tasks. Furthermore, students will learn how to develop 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. Aligned with this objective, the course does not concentrate exclusively on any single product; however, Windows Server serves as the primary platform for demonstrating the techniques taught, as it is the common foundation for all these products.

This course equips IT Professionals with the skills to manage Azure subscriptions, secure identities, administer infrastructure, configure virtual networking, connect Azure to 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 solutions.

Designed for Azure Administrators, this training covers the management, implementation, 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 empowers Azure administrators with the skills to plan, deliver, and manage virtual desktop experiences and remote applications across any device on Azure. Through a combination of demonstrations and hands-on labs, students will gain practical experience in deploying virtual desktops and applications on Azure Virtual Desktop, optimizing their performance in multi-session virtual environments.

This course empowers Solutions Architects to translate business requirements into secure, scalable, and reliable solutions. The curriculum covers virtualization, automation, networking, storage, identity, security, data platforms, and application infrastructure. Participants will gain insights into how decisions in each area impact the overall solution architecture.

Audience profile

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

This course equips Solutions Architects with the skills to translate business requirements into secure, scalable, and reliable solutions. The curriculum covers design considerations for logging, cost analysis, authentication, authorisation, governance, security, storage, high availability, and migration. Professionals in this role must make critical decisions across multiple domains to shape a cohesive design strategy.

Skills gained

• Design a governance solution.
• Design a compute solution.
• Design an application architecture.
• Design storage, non-relational and relational.
• Design data integration solutions.
• Design authentication, authorization, and identity solutions.
• Design network solutions.
• Design backup and disaster recovery solutions.
• Design monitoring solutions.
• Design migration solutions.

This instructor-led, live training in South Africa (online or on-site) is aimed at beginner-level / intermediate-level / advanced-level developers and technical professionals who wish to use Microsoft Azure to build, test, and deploy AI agents for business applications.

By the end of this training, participants will be able to: understand AI agent architecture on Azure, create and configure a working agent, connect agents to business knowledge sources, evaluate and prepare agents for deployment.

This instructor-led, live training in South Africa (online or onsite) is aimed at DevOps engineers, developers, and project managers who wish to utilize Azure DevOps to build and deploy optimized enterprise applications faster than traditional development approaches.

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

• Understand the fundamental DevOps vocabulary and principles.
• Install and configure the necessary Azure DevOps tools for software development.
• Utilize Azure DevOps tools and services to continuously adapt to the market.
• Build enterprise applications and evaluate current development processes upon Azure DevOps solutions.
• Manage teams more efficiently and accelerate software deployment time.
• Adopt DevOps development practices within the organization.

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 operationalise complete ML pipelines using Docker.

Upon completing this training, participants will be able to:

• Containerise 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 within containerised environments.

Course Format

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

Course Customisation Options

• For customised training tailored to specific ML infrastructure requirements, 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-level professionals aiming to build robust ML workflows utilizing Kubeflow.

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

• Navigate the Kubeflow ecosystem and its core components.
• Construct reproducible workflows using Kubeflow Pipelines.
• Execute scalable training jobs on Kubernetes.
• Deploy machine learning models efficiently via Kubeflow Serving.

Course Format

• Guided presentations alongside collaborative discussions.
• Hands-on labs featuring real Kubeflow components.
• Practical exercises to develop end-to-end ML workflows.

Course Customization Options

• Bespoke versions of this training can be arranged to suit your team’s technology stack and project requirements.

This instructor-led live training in South Africa (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 South Africa (online or at your premises) 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 via containerized pipelines and GitOps workflows.

This instructor-led, live training (available online or onsite) is designed for intermediate-level practitioners seeking 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 focused on building real-world CI/CD workflows.
• Live-lab practice for deploying ML workloads to Kubernetes.

Course Customisation Options

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