MLOps for Azure Machine Learning Training Course

This instructor-led, live training in Romania (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 Romania (online or onsite) is designed for intermediate to advanced-level professionals who aim to build and deploy AI-powered cloud applications using Microsoft Azure.

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

• Create event-driven and serverless applications using Azure Functions.
• Administer Azure storage solutions and virtual machines.
• Deploy and scale web applications using Azure App Service and Docker containers.
• Incorporate AI, machine learning, and natural language processing capabilities using Azure AI Services.
• Utilize GitHub Copilot to support AI-driven cloud application development.

This instructor-led, live training in Romania (online or onsite) is designed for developers and technical professionals at beginner, intermediate, and advanced levels who aim 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 the architecture of AI agents on Azure, create and configure a functional agent, link agents to business knowledge sources, and evaluate and prepare agents for deployment.

This training program enables participants to enhance their skills in designing Microsoft Azure solutions.

Upon completion, delegates will gain a thorough understanding of Azure service features and capabilities, allowing them to evaluate trade-offs and make informed decisions when designing public and hybrid cloud solutions.

Throughout the course, learners will define appropriate infrastructure and platform solutions that address functional, operational, and deployment requirements across the solution lifecycle.

This instructor-led, live training in Romania (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 Romania (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:

• Administer 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 Romania (online or onsite) targets security administrators seeking to learn how to configure Azure cloud security for protecting 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 integrates the capabilities of the Microsoft Bot Framework and Azure Functions, offering a robust platform for rapidly developing intelligent bots.

During this instructor-led live training, attendees will examine methods for efficiently creating intelligent bots within the Microsoft Azure ecosystem.

Upon completion of the training, participants will be capable of:

Grasping the fundamental principles behind intelligent bots.

Constructing intelligent bots using cloud-based applications.

Acquiring practical expertise in the Microsoft Bot Framework, the Bot Builder SDK, and Azure Bot Service.

Implementing established bot design patterns in real-world scenarios.

Developing and deploying their initial intelligent bot using Microsoft Azure.

Target Audience

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

Course Format

The training blends lectures and discussions with exercises, placing a strong emphasis on hands-on practice.

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

Upon completing this training, participants will be able to:

• Grasp 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 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 onsite, targets intermediate to advanced technical professionals seeking to containerize and operationalize entire ML pipelines using Docker.

Upon completing this training, participants will be equipped to:

• Containerize workloads related to ML training, validation, and inference.
• Design and orchestrate end-to-end ML pipelines utilizing Docker and complementary tools.
• Implement versioning, reproducibility, and CI/CD practices for ML components.
• Deploy, monitor, and scale ML services within containerized environments.

Format of the Course

• Interactive lectures supported by practical demonstrations.
• Hands-on exercises focused on building real-world 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 live, instructor-led training in Romania (online or onsite) is designed for intermediate Java developers, software engineers, and cloud enthusiasts who want to leverage Azure OpenAI to build intelligent applications.

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

• Understand the core 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 Romania (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 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 Romania (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 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 online or onsite) is designed for intermediate-level practitioners seeking to build automated, scalable MLOps pipelines on Kubernetes.

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

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

Course Format

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

Course Customization Options

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