Kubeflow on Azure Training Course

This instructor-led, live training in the UAE (online or onsite) is designed for advanced 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 capable of:

• Optimizing DeepSeek models for efficiency, accuracy, and scalability.
• Applying best practices for MLOps and model versioning.
• Deploying DeepSeek models across cloud and on-premise infrastructures.
• Effectively monitoring, maintaining, and scaling AI solutions.

This instructor-led, live training in the UAE (online or onsite) targets intermediate to advanced professionals who wish 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 instructor-led, live training in the UAE (online or onsite) is designed for engineers looking to leverage Azure ML's drag-and-drop platform to deploy Machine Learning workloads. Participants will avoid the need to purchase software and hardware, while also bypassing concerns related to maintenance and deployment.

Upon completing this training, participants will be able to:

• Develop highly accurate machine learning models using Python, R, or zero-code tools.
• Utilize Azure's available datasets and algorithms to train and track machine learning and deep-learning models.
• Use Azure's interactive workspace to collaboratively develop ML models.
• Select from various Azure-supported ML frameworks, including PyTorch, TensorFlow, and scikit-learn.

This course equips students with the essential knowledge and skills needed to utilize PowerShell for administering and automating tasks on Windows servers. Participants will develop the ability to identify and construct the precise commands required for specific operational tasks. Additionally, students will learn to write scripts that handle advanced duties, such as automating repetitive processes and generating comprehensive reports. By providing prerequisite skills that support a wide array of Microsoft products—including Windows Server, Windows Client, Microsoft Azure, and Microsoft 365—this course aims to broaden technical proficiency. Accordingly, the curriculum does not center on any single product; however, it utilizes Windows Server, the common platform for these technologies, as the primary example to illustrate the taught techniques.

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

Designed specifically for Azure Administrators, this course covers the implementation, management, and monitoring of identity, governance, storage, compute, and virtual networks within cloud environments. Azure Administrators will learn how to provision, size, monitor, and optimize resources effectively.

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 seeking 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 utilizing Docker and supporting tools.
• Implement versioning, reproducibility, and CI/CD practices for ML components.
• Deploy, monitor, and scale ML services within containerized environments.

Course Format

• 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.

This instructor-led live training in the UAE (online or onsite) is designed for developers and data scientists who aim to build, deploy, and manage machine learning workflows on Kubernetes.

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

• Install and configure Kubeflow on-premise and in the cloud using AWS EKS (Elastic Kubernetes Service).
• Construct, 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 the UAE (online or onsite) is designed for engineers who wish to deploy Machine Learning workloads to an AWS EC2 server.

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

• Install and configure Kubernetes, Kubeflow, and other necessary software on AWS.
• Use EKS (Elastic Kubernetes Service) to simplify the process of initializing a Kubernetes cluster on AWS.
• Create and deploy a Kubernetes pipeline for automating and managing ML models in production.
• Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.
• Leverage other AWS managed services to extend an ML application.

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 the UAE (online or onsite) is designed for 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 (online or onsite) is aimed at data scientists who wish to go beyond building ML models and optimize the ML model creation, tracking, and deployment process.

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

• Install and configure MLflow and related ML libraries and frameworks.
• Appreciate the importance of trackability, reproducibility, and deployability of an ML model.
• Deploy ML models to different public clouds, platforms, or on-premise servers.
• Scale the ML deployment process to accommodate multiple users collaborating on a project.
• Set up a central registry to experiment with, reproduce, and deploy ML models.

This instructor-led live training in the UAE (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.

This instructor-led, live training (available online or onsite) is designed for machine learning engineers aiming to utilize Azure Machine Learning and Azure DevOps to facilitate MLOps practices.

Upon completing this training, participants will be able to:

• Construct reproducible workflows and machine learning models.
• Oversee the machine learning lifecycle.
• Track and report model version history, assets, and more.
• Deploy production-ready machine learning models in any environment.

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 both online and onsite, targets intermediate practitioners seeking to construct automated, scalable MLOps pipelines on Kubernetes.

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

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

Course Format

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

Course Customization Options

• Organizations can request customized content that aligns with their internal MLOps tools and infrastructure.