MLOps for Azure Machine Learning Training Course

This instructor-led, live training in Malaysia (online or onsite) targets 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 Malaysia (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 Malaysia (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 empowers participants to enhance their Microsoft Azure solution design expertise.

Upon completion, delegates will grasp the features and capabilities of Azure services, enabling them to identify trade-offs and make informed decisions for designing public and hybrid cloud solutions.

Throughout the training, appropriate infrastructure and platform solutions to meet functional, operational, and deployment requirements across the solution lifecycle will be defined.

This instructor-led, live training in Malaysia (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 Malaysia (online or onsite) is designed for security administrators who want to secure their Azure workloads.

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

• Administer host security, network security, and other areas.
• Set up storage and database security in Azure.
• Implement security monitoring using Azure resources.
• Prevent malicious cyber attacks on data and infrastructure.

This instructor-led, live training in Malaysia (online or on-site) is designed for 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 integrates the Microsoft Bot Framework with Azure Functions, offering a robust platform for rapidly creating intelligent bots.

Through this instructor-led live training, participants will learn how to efficiently develop smart bots using Microsoft Azure.

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

Grasp the fundamental concepts underlying intelligent bots.

Develop intelligent bots using cloud-based applications.

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

Implement established bot design patterns in practical scenarios.

Create and deploy their first intelligent bot using Microsoft Azure.

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

This instructor-led training session, available online or on-site, targets intermediate to advanced technical professionals seeking to containerize and operationalise complete ML pipelines using Docker.

Upon completing this training, participants will gain the ability to:

• Containerise ML training, validation, and inference workloads.
• 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 containerised environments.

Course Format

• Interactive lectures complemented by practical demonstrations.
• Hands-on exercises centred on constructing real-world ML pipeline components.
• Live-lab implementation of end-to-end containerised workflows.

Course Customization Options

• For training tailored to specific ML infrastructure requirements, please contact us to discuss available options.

This instructor-led, live training in Malaysia (online or onsite) is designed for intermediate-level Java developers, software engineers, and cloud enthusiasts eager to leverage Azure OpenAI to build intelligent applications.

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

• Comprehend the principles of Generative AI and its practical applications.
• Configure and administer an Azure OpenAI service.
• Integrate OpenAI models into Java applications.
• Deploy AI-driven 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

• Customised versions of this training can be arranged to align with your team’s technology stack and project requirements.

This instructor-led live training in Malaysia (online or onsite) 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 Malaysia (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 serves as a framework that automates the training, validation, packaging, and deployment of machine learning models through containerized pipelines and GitOps workflows.

This instructor-led live training, available both online and onsite, is designed for intermediate-level practitioners looking to construct automated, scalable MLOps pipelines on Kubernetes.

Upon completing this training, participants will gain the ability to:

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

Course Format

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

Customization Options

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