MLOps for Azure Machine Learning Training Course

About This Course

In this course, students will gain the skills to design and develop services that can access local and remote data from a variety of sources. They will also learn how to develop and deploy these services in hybrid environments, encompassing both on-premises servers and Microsoft Azure.

Audience Profile

Primary: .NET developers who are interested in learning how to develop and deploy services to hybrid environments.
Secondary: .NET developers with experience in web application development who are looking to explore the creation of new applications or the migration of existing ones to Microsoft Azure.

At Course Completion

Upon completing this course, students will be able to:

• Explain the fundamental concepts of service development and data access strategies using the .NET platform.
• Understand the Microsoft Azure cloud platform and its offerings in compute, data, and application hosting.
• Design and develop a data-centric application using Visual Studio 2017 and Entity Framework Core.
• Create, implement, and consume HTTP services with ASP.NET Core.
• Extend HTTP services using ASP.NET Core.
• Host services both on-premises and in Microsoft Azure.
• Deploy services to both on-premises and cloud environments, and manage the interfaces and policies for their services.
• Select a data storage solution, cache, distribute, and synchronize data effectively.
• Monitor, log, and troubleshoot services efficiently.
• Understand 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 Uzbekistan (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 skills for designing Azure AI solutions by creating a customer support chatbot that leverages artificial intelligence from the Microsoft Azure platform. This includes utilizing language understanding and pre-built AI capabilities available in Azure Cognitive Services.

About This Course

This course delves into the foundational concepts of artificial intelligence (AI) and explores the various Microsoft Azure services that can be utilized to develop AI solutions. It is not intended to turn students into professional data scientists or software developers but rather to enhance their understanding of common AI applications and equip them with the skills to identify suitable Azure services for these tasks. The course combines instructor-led sessions with online resources from the Microsoft Learn platform (https://azure.com/learn). Practical exercises are based on modules from the Learn platform, and students are encouraged to use this content as reference material to reinforce their learning and delve deeper into specific topics.

Audience Profile

The Azure AI Fundamentals course is tailored for anyone interested in understanding the types of solutions that artificial intelligence (AI) can enable and the services available on Microsoft Azure to create these solutions. No prior experience with Microsoft Azure is required, but a basic familiarity with computer technology and the Internet is assumed. Some concepts covered in the course involve a basic understanding of mathematics, such as interpreting charts. The course includes hands-on activities that involve data manipulation and running code, so a foundational knowledge of programming principles will be beneficial.

At Course Completion

Upon completing this course, you will be able to:

• Describe the workloads and considerations involved in artificial intelligence (AI).
• Explain the fundamental principles of machine learning on Azure.
• Discuss the features of computer vision tasks on Azure.
• Outline the capabilities of Natural Language Processing (NLP) workloads on Azure.
• Describe the features of conversational AI tasks on Azure.

Docker is a containerization platform used to create reproducible, portable, and scalable environments for machine learning systems.

This instructor-led, live training (conducted online or on-site) is designed for intermediate to advanced technical professionals who aim to containerize and operationalize complete ML pipelines using Docker.

Upon completing this training, participants will be able to:

• Containerize machine learning training, validation, and inference workloads.
• Design and orchestrate end-to-end machine learning pipelines using Docker and supporting tools.
• Implement versioning, reproducibility, and CI/CD for machine learning components.
• Deploy, monitor, and scale machine learning 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 machine learning infrastructure needs, please contact us to discuss options.

This instructor-led, live training in Uzbekistan (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 Uzbekistan (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 Uzbekistan (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to Azure cloud.

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

• Install and configure Kubernetes, Kubeflow and other needed software on Azure.
• Use Azure Kubernetes Service (AKS) to simplify the work of initializing a Kubernetes cluster on Azure.
• 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 simplify the process of building, training, and deploying machine learning workloads on Kubernetes.

This instructor-led, live training (available online or onsite) is tailored for professionals at beginner to intermediate levels who want to create reliable ML workflows using Kubeflow.

By the end of this training, participants will have acquired the skills to:

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

Course Format

• Interactive presentations and group discussions.
• Hands-on labs using real Kubeflow components.
• Practical exercises to construct end-to-end ML workflows.

Customization Options for the Course

• Customized versions of this training can be arranged to match your team’s technology stack and project needs.

This instructor-led, live training in Uzbekistan (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 (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 Uzbekistan (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 is a framework designed to automate the training, validation, packaging, and deployment of machine learning models using containerized pipelines and GitOps workflows.

This instructor-led, live training (available online or onsite) is targeted at intermediate-level practitioners who aim to build automated, scalable MLOps pipelines on Kubernetes.

Upon completing this training, participants will be able to:

• Design comprehensive CI/CD pipelines for machine learning processes.
• Implement GitOps workflows for model deployment and version control.
• Automate the training, testing, and packaging of machine learning models.
• Integrate monitoring, alerting, and rollback strategies into their pipelines.

Format of the Course

• Instructor-guided presentations and in-depth technical discussions.
• Hands-on exercises that develop real-world CI/CD workflows.
• Live-lab practice for deploying machine learning workloads to Kubernetes.

Course Customization Options

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