MLOps for Azure Machine Learning Training Course

This instructor-led, live training in Pakistan (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 Pakistan (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 Pakistan (online or onsite) is aimed at engineers who wish to use Azure ML's drag-and-drop platform to deploy Machine Learning workloads without having to purchase software and hardware and without having to worry about maintenance and deployment.

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

• Write highly-accurate machine learning models using Python, R, or zero-code tools.
• Leverage Azure's available data sets and algorithms to train and track machine learning and deep-learning models.
• Use Azures interactive workspace to collaboratively develop ML models.
• Choose from different Azure-supported ML frameworks such as PyTorch, TensorFlow, and scikit-learn.

This course provides the knowledge and skills to design and implement DevOps processes and practices. Students will learn how to plan for DevOps, use source control, scale Git for an enterprise, consolidate artifacts, design a dependency management strategy, manage secrets, implement continuous integration, implement a container build strategy, design a release strategy, set up a release management workflow, implement a deployment pattern, and optimize feedback mechanisms.

Audience profile

Students in this course are interested in designing and implementing DevOps processes or in passing the Microsoft Azure DevOps Solutions certification exam.

Job role: DevOps Engineer

Preparation for exam: AZ-400

Skills gained

• Plan for the transformation with shared goals and timelines
• Select a project and identify project metrics and Key Performance Indicators (KPI's)
• Create a team and agile organizational structure
• Design a tool integration strategy
• Design a license management strategy (e.g. Azure DevOps and GitHub users)
• Design a strategy for end-to-end traceability from work items to working software
• Design an authentication and access strategy
• Design a strategy for integrating on-premises and cloud resources
• Describe the benefits of using Source Control
• Describe Azure Repos and GitHub
• Migrate from TFVC to Git
• Manage code quality including technical debt SonarCloud, and other tooling solutions
• Build organizational knowledge on code quality
• Explain how to structure Git repos
• Describe Git branching workflows
• Leverage pull requests for collaboration and code reviews
• Leverage Git hooks for automation
• Use Git to foster inner source across the organization
• Explain the role of Azure Pipelines and its components
• Configure Agents for use in Azure Pipelines
• Explain why continuous integration matters
• Implement continuous integration using Azure Pipelines
• Define Site Reliability Engineering
• Design processes to measure end-user satisfaction and analyze user feedback
• Design processes to automate application analytics
• Manage alerts and reduce meaningless and non-actionable alerts
• Carry out blameless retrospectives and create a just culture
• Define an infrastructure and configuration strategy and appropriate toolset for a release pipeline and application infrastructure
• Implement compliance and security in your application infrastructure
• Describe the potential challenges with integrating open-source software
• Inspect open-source software packages for security and license compliance
• Manage organizational security and compliance policies
• Integrate license and vulnerability scans into build and deployment pipelines
• Configure build pipelines to access package security and license ratings

This course provides IT Security Professionals with the knowledge and skills needed to implement security controls, maintain an organization’s security posture, and identify and remediate security vulnerabilities. This course includes security for identity and access, platform protection, data and applications, and security operations.

Audience profile

This course is for Azure Security Engineers who are planning to take the associated certification exam, or who are performing security tasks in their day-to-day job. This course would also be helpful to an engineer that wants to specialize in providing security for Azure-based digital platforms and play an integral role in protecting an organization's data.

Skills gained

• Implement enterprise governance strategies including role-based access control, Azure policies, and resource locks.
• Implement an Azure AD infrastructure including users, groups, and multi-factor authentication.
• Implement Azure AD Identity Protection including risk policies, conditional access, and access reviews.
• Implement Azure AD Privileged Identity Management including Azure AD roles and Azure resources.
• Implement Azure AD Connect including authentication methods and on-premises directory synchronization.
• Implement perimeter security strategies including Azure Firewall.
• Implement network security strategies including Network Security Groups and Application Security Groups.
• Implement host security strategies including endpoint protection, remote access management, update management, and disk encryption.
• Implement container security strategies including Azure Container Instances, Azure Container Registry, and Azure Kubernetes.
• Implement Azure Key Vault including certificates, keys, and secretes.
• Implement application security strategies including app registration, managed identities, and service endpoints.
• Implement storage security strategies including shared access signatures, blob retention policies, and Azure Files authentication.
• Implement database security strategies including authentication, data classification, dynamic data masking, and always encrypted.
• Implement Azure Monitor including connected sources, log analytics, and alerts.
• Implement Azure Security Center including policies, recommendations, and just in time virtual machine access.
• Implement Azure Sentinel including workbooks, incidents, and playbooks.

Microsoft Azure Infrastructure and 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 Pakistan (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 Pakistan (online or onsite) is aimed at developers who wish to learn how to build microservices on Microsoft Azure Service Fabric (ASF).

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

• Use ASF as a platform for building and managing microservices.
• Understand key microservices programming concepts and models.
• Create a cluster in Azure.
• Deploy microservices on premises or in the cloud.
• Debug and troubleshoot a live microservice application.

This instructor-led, live training in Pakistan (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 using AWS EKS (Elastic Kubernetes Service).
• 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 Pakistan (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 Pakistan (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to an AWS EC2 server.

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

• Install and configure Kubernetes, Kubeflow and other needed software on AWS.
• Use EKS (Elastic Kubernetes Service) to simplify the work of initializing 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 running in parallel.
• Leverage other AWS managed services to extend an ML application.

This instructor-led, live training in Pakistan (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to Azure cloud.

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

• Install and configure Kubernetes, Kubeflow and other needed software on Azure.
• Use Azure Kubernetes Service (AKS) to simplify the work of initializing 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.