MLOps for Azure Machine Learning Training Course

This instructor-led, live training in Finland (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.

This instructor-led, live training in Finland (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 Finland (online or onsite) 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 training permits delegates to improve their Microsoft Azure solution design skills.

After this training the delegate will understand the features and capabilities of Azure services, to be able to identify trade-offs, and make decisions for designing public and hybrid cloud solutions.

During training the appropriate infrastructure and platform solutions to meet the required functional, operational, and deployment requirements through the solution life-cycle will be defined.

This instructor-led, live training in Finland (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.

This instructor-led, live training in Finland (online or onsite) is aimed at security administrators who wish to secure Azure workloads.

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

• Administrate host security, network security, and more.
• Set up storage and database security in Azure.
• Implement security monitoring using Azure resources.
• Prevent malicious cyber attacks on data and infrastructures.

This instructor-led, live training in Finland (online or onsite) is aimed at security administrators who wish to learn how to configure Azure cloud security to secure workloads running in Azure.

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

• Configure host and network security.
• Configure Azure advanced security options.
• Use Azure to secure cloud computing workloads.
• Use endpoint protection services security against malware and viruses.
• Secure container workloads that are running in Azure.

Azure Bot Service brings together the capabilities of the Microsoft Bot Framework and Azure Functions, providing a powerful platform for quickly building intelligent bots.

In this instructor-led, live training, participants will explore how to efficiently develop intelligent bots using Microsoft Azure.

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

Understand the core concepts behind intelligent bots.

Build intelligent bots using cloud-based applications.

Gain practical knowledge of the Microsoft Bot Framework, the Bot Builder SDK, and Azure Bot Service.

Apply established bot design patterns in real-world scenarios.

Create and deploy their first intelligent bot using Microsoft Azure.

Audience

This course is designed for developers, hobbyists, engineers, and IT professionals interested in bot development.

Format of the course

The training combines lectures and discussions with exercises and a strong emphasis on hands-on practice.

This instructor-led, live training in Finland (online or onsite) is aimed at intermediate-level data engineers who wish to learn how to use Azure Data Lake Storage Gen2 for effective data analytics solutions.

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

• Understand the architecture and key features of Azure Data Lake Storage Gen2.
• Optimize data storage and access for cost and performance.
• Integrate Azure Data Lake Storage Gen2 with other Azure services for analytics and data processing.
• Develop solutions using the Azure Data Lake Storage Gen2 API.
• Troubleshoot common issues and optimize storage strategies.

Docker is a containerization platform used to build reproducible, portable, and scalable environments for ML systems.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level technical professionals who wish to containerize and operationalize complete ML pipelines using Docker.

Upon completion of 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 supporting tools.
• Implement versioning, reproducibility, and CI/CD for ML components.
• Deploy, monitor, and scale ML 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 ML infrastructure needs, please contact us to discuss options.

This instructor-led, live training in Finland (online or onsite) is aimed at intermediate-level Java developers, software engineers, and cloud enthusiasts who wish to harness the power of Azure OpenAI to create intelligent applications.

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

• Understand the principles of Generative AI and its applications.
• Set up and manage an Azure OpenAI service.
• Integrate OpenAI's models into Java applications.
• Deploy AI-powered features within web applications.

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 Finland (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 Finland (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 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.

After 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.

Format of the Course

• Instructor-guided presentations and technical deep dives.
• Hands-on exercises that build real-world CI/CD workflows.
• Live-lab practice deploying ML workloads to Kubernetes.

Course Customization Options

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