MLOps for Azure Machine Learning Training Course

This instructor-led live training in Brazil (online or onsite) targets AI engineers and data scientists at an advanced level who possess intermediate-to-advanced experience and aim to enhance DeepSeek model performance, reduce latency, and deploy AI solutions efficiently using modern MLOps practices.

Upon completing this training, participants will be able to:

• Optimize DeepSeek models for efficiency, accuracy, and scalability.
• Apply best practices for MLOps and model versioning.
• Deploy DeepSeek models across cloud and on-premise infrastructure.
• Effectively monitor, maintain, and scale AI solutions.

This instructor-led, live training in Brazil (online or onsite) is designed for intermediate 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 course guides Solutions Architects in transforming business requirements into secure, scalable, and reliable solutions. The curriculum covers virtualization, automation, networking, storage, identity, security, data platforms, and application infrastructure. It also explains how decisions within each of these areas impact the overall solution design.

Audience profile

This course is designed for IT Professionals who specialize in designing and implementing solutions on Microsoft Azure. Participants should possess a broad understanding of IT operations, including networking, virtualization, identity, security, business continuity, disaster recovery, data platforms, budgeting, and governance. Azure Solution Architects utilize the Azure Portal and, as their proficiency grows, the Command Line Interface. Candidates must demonstrate expert-level skills in Azure administration along with experience in Azure development and DevOps processes.

This instructor-led, live training in Brazil (online or onsite) is aimed at beginner-level, intermediate-level, and 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 instructor-led, live training in Brazil (online or onsite) is designed for DevOps engineers, developers, and project managers who aim to leverage Azure DevOps to build and deploy optimized enterprise applications more rapidly than traditional development methods allow.

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

• Comprehend the core DevOps vocabulary and principles.
• Install and configure the necessary Azure DevOps tools for software development.
• Use Azure DevOps tools and services to continuously adapt to market changes.
• Develop enterprise applications and assess current development processes using Azure DevOps solutions.
• Manage teams more efficiently and reduce software deployment time.
• Implement DevOps development practices within their organization.

This instructor-led live training in Brazil (online or on-site) is tailored for security administrators eager to learn how to configure Azure cloud security to protect workloads running in Azure.

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

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

Azure Bot Service integrates the capabilities of the Microsoft Bot Framework and Azure Functions to offer a robust platform for rapidly constructing intelligent bots.

During this instructor-led live training, attendees will discover how to effectively develop intelligent bots using Microsoft Azure.

Upon completing the training, participants will be able to:

Comprehend the fundamental concepts underlying intelligent bots.

Construct intelligent bots leveraging cloud-based applications.

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

Implement established bot design patterns within real-world scenarios.

Develop and deploy their initial intelligent bot utilizing Microsoft Azure.

Audience

This course is tailored for developers, hobbyists, engineers, and IT professionals with an interest 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 Brazil (online or onsite) targets 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 serves as a containerization platform designed to build reproducible, portable, and scalable environments for machine learning systems.

This instructor-led live training, available either online or on-site, targets intermediate to advanced technical professionals looking 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 supported 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 training tailored to specific ML infrastructure requirements, please contact us to discuss available options.

This instructor-led, live training in Brazil (online or onsite) is designed for intermediate-level Java developers, software engineers, and cloud enthusiasts who want 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.

In this instructor-led, live training at Brazil (onsite or remote), participants will explore the core concepts, components, and services of Microsoft Azure while building a sample cloud application from start to finish.

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

• Grasp the fundamentals of Microsoft Azure
• Identify various Azure tools and services
• Learn how to leverage Azure for developing cloud-based 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 Brazil (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.
• Use Kubeflow to spawn and manage Jupyter notebooks.
• Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led, live training session in Brazil (available online or in-person) is designed for engineers who wish to evaluate the approaches and tools currently available, helping them make informed decisions on how to proceed with adopting MLOps within their organizations.

Upon completing this training, participants will be able to:

• Install and set up various MLOps frameworks and tools.
• Build a team with the appropriate skills to construct and support an MLOps infrastructure.
• Prepare, validate, and version data for use by Machine Learning models.
• Comprehend the components of a Machine Learning Pipeline and identify the necessary tools to create one.
• Experiment with different Machine Learning frameworks and servers for production deployment.
• Operationalize the entire Machine Learning lifecycle to ensure it is reproducible and maintainable.

MLOps on Kubernetes is a framework designed to automate the training, validation, packaging, and deployment of machine learning models through containerized pipelines and GitOps workflows.

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

Upon completing 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 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.