Kubeflow on Azure Training Course

About This Course

In this course, students will learn how to design and develop services that access local and remote data from various sources. Students will also learn how to develop and deploy services to hybrid environments, including on-premises servers and Microsoft Azure.

Audience Profile

Primary: .NET developers who want to learn how to develop services and deploy them to hybrid environments.
Secondary: .NET developers with Web application development experience who are exploring developing new applications or porting existing applications to Microsoft Azure.

At Course Completion

After completing this course, students will be able to:

• Describe the basic concepts of service development and data access strategies using the .NET platform.
• Describe the Microsoft Azure cloud platform and its compute, data, and application hosting offerings.
• Design and develop a data-centric 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 interface and policy for their services.
• Choose a data storage solution, cache, distribute, and synchronize data.
• Monitor, log, and troubleshoot services.
• Describe claim-based identity concepts and standards, and implement authentication and authorization with Azure Active Directory.
• Create scalable service applications.

This instructor-led, live training in Kazakhstan (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.

Gain the necessary knowledge for designing Azure AI solution by building a customer support chat Bot using artificial intelligence from the Microsoft Azure platform including language understanding and pre-built AI functionality in the Azure Cognitive Services.

This instructor-led, live training in Kazakhstan (online or onsite) is aimed at intermediate-level to advanced-level 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 Kazakhstan (online or onsite) is aimed at engineers who wish to use Azure ML's drag-and-drop platform to deploy Machine Learning workloads without having to purchase software and hardware and without having to worry about maintenance and deployment.

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

• Write highly-accurate machine learning models using Python, R, or zero-code tools.
• Leverage Azure's available data sets and algorithms to train and track machine learning and deep-learning models.
• Use Azures interactive workspace to collaboratively develop ML models.
• Choose from different Azure-supported ML frameworks such as PyTorch, TensorFlow, and scikit-learn.

About This Course

This two-day course is designed for AWS Sysops administrators interested in learning how Azure is different from AWS, and how Azure is administered. The workshops main topics are Azure Administration, Azure Networking, Azure Compute, Azure Storage, and Azure Governance. This workshop combines lecture with hands-on practical exercises and discussion/review. During the workshop students will build an end-to-end architecture that demonstrates the main features discussed in the course.

Audience Profile

The audience for this course is an AWS Sysops Administrator Associate or equivalent. This person has one to two years of experience in AWS deployment, management, and operations. Students taking this course are interested in learning how Azure is different from AWS, and how Azure is administered. Students may also be interested in taking the AZ-103 Microsoft Azure Administrator certification exam, or the AZ-900 Azure Fundamentals exam.

About This Course

A three-day course designed to teach AWS (Amazon Web Services) developers how to prepare end-to-end solutions in Microsoft Azure. In this course you will construct Azure App Service Web App solutions and Azure Functions, use blob or Cosmos DB storage in solutions, implement secure cloud solutions that include user authentication and authorization, implement API management, and develop event- and message-based solutions, and monitor, troubleshoot, and optimize your Azure solutions. You will learn how developers use Azure services, with additional focus on features and tasks that differ from AWS, and what that means for you as you develop applications that will be hosted by using Azure services.

Audience Profile

Students in this course are experienced AWS developers interested in Azure development.

This instructor-led, live training in Kazakhstan (online or onsite) is aimed at developers who wish to learn how to build microservices on Microsoft Azure Service Fabric (ASF).

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

• Use ASF as a platform for building and managing microservices.
• Understand key microservices programming concepts and models.
• Create a cluster in Azure.
• Deploy microservices on premises or in the cloud.
• Debug and troubleshoot a live microservice application.

This instructor-led, live training in Kazakhstan (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 using AWS EKS (Elastic Kubernetes Service).
• 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 Kazakhstan (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 Kazakhstan (online or onsite) is aimed at 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 needed software on AWS.
• Use EKS (Elastic Kubernetes Service) to simplify the work 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.

This instructor-led, live training in (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, reproducability 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 Kazakhstan (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 in (online or onsite) is aimed at machine learning engineers who wish to use Azure Machine Learning and Azure DevOps to facilitate MLOps practices.

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

• Build reproducible workflows and machine learning models.
• Manage the machine learning lifecycle.
• Track and report model version history, assets, and more.
• Deploy production ready machine learning models anywhere.