MLOps for Azure Machine Learning 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 Uzbekistan (online or onsite) is designed for advanced-level AI engineers and data scientists with intermediate-to-advanced experience who wish to enhance DeepSeek model performance, minimize latency, and efficiently deploy AI solutions 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 knowledge required for designing Azure AI solutions by constructing a customer support chatbot utilizing artificial intelligence from the Microsoft Azure platform. This includes leveraging language understanding capabilities and pre-built AI functionality available through Azure Cognitive Services.

About This Course

This course introduces fundamental concepts related to artificial intelligence (AI) and the services available in Microsoft Azure for building AI solutions. It is not intended to train students to become professional data scientists or software developers; rather, it aims to raise awareness of common AI workloads and equip learners with the ability to identify appropriate Azure services to support them. The course is structured as a blended learning experience, combining instructor-led sessions with online resources 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 reference material 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 artificial intelligence (AI) enables and the Microsoft Azure services that can be used to create them. No prior experience with Microsoft Azure is required; however, a basic familiarity with computer technology and the Internet is expected. Some concepts covered in the course require a foundational understanding of mathematics, such as the ability to interpret charts. The course includes practical activities involving data handling and code execution, so a basic knowledge of programming principles will be beneficial.

At Course Completion

Upon completing this course, you will be able to:

• Describe AI workloads and key considerations
• Explain the fundamental principles of machine learning on Azure
• Outline the features of computer vision workloads on Azure
• Outline the features of Natural Language Processing (NLP) workloads on Azure
• Outline the features of conversational AI workloads on Azure

Docker serves as a containerization platform designed to create reproducible, portable, and scalable environments for machine learning systems.

This instructor-led live training, available both online and on-site, 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 using Docker and complementary tools.
• Implement versioning, reproducibility, and CI/CD practices for ML components.
• Deploy, monitor, and scale ML services within containerized environments.

Course Format

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

Customization Options

• For tailored training aligned with specific ML infrastructure requirements, please contact us to explore options.

This instructor-led, live training in Uzbekistan (online or on-site) 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 both on-premises and in the cloud using AWS EKS (Elastic Kubernetes Service).
• Build, deploy, and manage ML workflows leveraging Docker containers and Kubernetes.
• Execute complete machine learning pipelines across diverse architectures and cloud environments.
• Utilize Kubeflow to spawn and manage Jupyter notebooks.
• Develop ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led, live training in Uzbekistan (online or onsite) is targeted at engineers who aim to deploy Machine Learning workloads to an AWS EC2 server.

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

• Install and configure Kubernetes, Kubeflow, and other necessary software on AWS.
• Utilize EKS (Elastic Kubernetes Service) to streamline the initialization of 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 operating in parallel.
• Leverage other AWS managed services to extend an ML application.

This instructor-led, live training in Uzbekistan (online or on-site) is designed for engineers who wish to deploy Machine Learning workloads to the Azure cloud.

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

• Install and configure Kubernetes, Kubeflow, and other required software on Azure.
• Use Azure Kubernetes Service (AKS) to simplify the initialization of 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 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 Uzbekistan (online or onsite) is targeted at developers and data scientists who aim 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 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 (available online or on-site) is designed for data scientists who wish to move 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.
• Understand the importance of trackability, reproducibility, and deployability in ML models.
• Deploy ML models to various 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 on-site) is tailored for engineers seeking to evaluate current approaches and tools to make informed decisions about adopting MLOps within their organizations.

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

• Install and configure various MLOps frameworks and tools.
• Assemble the right team with the necessary skills to build and support an MLOps system.
• Prepare, validate, and version data for use by ML models.
• Understand the components of an ML Pipeline and the tools required to build one.
• Experiment with different machine learning frameworks and servers for deploying to production.
• Operationalize the entire Machine Learning process to ensure it is reproducible and maintainable.

MLOps on Kubernetes is a framework for automating the training, validation, packaging, and deployment of machine learning models using containerized pipelines and GitOps workflows.

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

Upon completing this training, participants will be equipped to:

• Design end-to-end CI/CD pipelines for machine learning.
• Implement GitOps workflows for model deployment and versioning.
• Automate training, testing, and packaging of ML models.
• Integrate monitoring, alerting, and rollback strategies.

Course Format

• Instructor-led presentations and in-depth technical sessions.
• Practical exercises for building real-world CI/CD workflows.
• Live lab sessions for deploying ML workloads on Kubernetes.

Course Customisation Options

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