Containers and Virtual Machines (VMs) Training in Almada

Edge AI represents a paradigm dedicated to executing machine learning inference near data sources, thereby achieving low-latency, efficient, and scalable processing.

This instructor-led live training (available online or onsite) targets intermediate to advanced practitioners seeking to deploy, orchestrate, and optimize AI workloads within Kubernetes-based edge environments.

Upon completing this course, participants will be able to:

• Configure lightweight Kubernetes distributions tailored for edge deployments.
• Effectively deploy AI inference workloads across resource-constrained edge nodes.
• Address connectivity challenges and implement synchronization patterns.
• Optimize performance, storage, and networking for real-world edge scenarios.

Course Format

• Guided presentations reinforced by real-world examples.
• Scenario-based labs and practical edge deployment exercises.
• Hands-on experience with Kubernetes edge frameworks.

Customization Options

• To request training customized to your specific edge platform requirements, please contact us to arrange.

Kubernetes serves as a container orchestration platform extensively utilized for managing distributed applications at scale.

This instructor-led, live training (available online or onsite) targets advanced practitioners aiming to leverage AI and machine learning techniques to enhance Kubernetes resource usage, scheduling choices, and autoscaling strategies.

Upon completing this program, participants will be able to:

• Apply AI/ML models to refine workload scheduling decisions within Kubernetes.
• Utilize predictive analytics to optimize CPU, GPU, and memory allocation.
• Implement intelligent autoscaling through reinforcement learning and metric forecasting.
• Lower infrastructure costs and latency via automated resource optimization.

Format of the Course

• Instructor-guided technical presentations and in-depth discussions.
• Hands-on lab work utilizing real Kubernetes clusters.
• Practical exercises applying AI models to actual operational scenarios.

Course Customization Options

• To tailor this course to your specific platform setup or operational needs, please contact us for customization.

MLOps on Kubernetes provides a framework 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 onsite, targets intermediate-level practitioners looking to build automated and scalable MLOps pipelines on Kubernetes.

Upon completion, 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.

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.

CI/CD for AI represents a structured methodology for automating model packaging, testing, containerisation, and deployment through continuous integration and continuous delivery pipelines.

This instructor-led, live training (available online or onsite) is designed for intermediate-level professionals seeking to automate end-to-end AI model delivery workflows using Docker and CI/CD platforms.

Upon completing the training, participants will be able to:

• Establish automated pipelines for constructing and testing AI model containers.
• Implement version control and reproducibility measures throughout the model lifecycle.
• Integrate automated deployment strategies for AI services.
• Apply CI/CD best practices specifically tailored to machine learning operations.

Course Format

• Instructor-guided presentations and technical discussions.
• Practical labs and hands-on implementation exercises.
• Realistic CI/CD workflow simulations within a controlled environment.

Course Customisation Options

• If your organisation requires tailored pipeline workflows or platform integrations, please contact us to customise this course.

This instructor-led, live training in Almada (online or onsite) is aimed at advanced-level Kubernetes administrators and DevOps engineers who wish to enhance their monitoring skills for Kubernetes clusters using Prometheus and Grafana.

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

• Set up Prometheus and Grafana for Kubernetes monitoring.
• Monitor key metrics for pods, nodes, and services.
• Create dynamic dashboards to visualise cluster health and performance.
• Implement alerting strategies for proactive issue resolution.
• Apply best practices for scaling monitoring solutions in Kubernetes environments.

Hybrid AI deployment involves executing AI inference across cloud, on-premise, and edge environments using unified container-based workflows.

This instructor-led, live training (available online or onsite) is designed for advanced professionals aiming to design and deploy distributed AI inference systems within heterogeneous environments.

Upon completing this training, participants will be able to:

• Construct secure and scalable containerized AI services for multi-location environments.
• Deploy AI inference workloads to cloud platforms, local servers, and edge devices using Docker.
• Integrate orchestration tools to automate distributed AI operations.
• Enhance inference latency, reliability, and resilience across diverse infrastructure.

Course Format

• Guided presentations and expert-led discussions.
• Extensive hands-on practice and applied exercises.
• Real-world experimentation within a controlled live-lab environment.

Course Customization Options

• To tailor this course to your organization’s specific infrastructure or use cases, please contact us to arrange customization.

Kubernetes is an open-source platform designed for the automated deployment, scaling, and management of containerized applications.

This instructor-led live training (available online or onsite) targets beginner to intermediate IT professionals eager to grasp the core concepts and components of Kubernetes and apply them to manage containerized applications at scale.

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

• Understanding Kubernetes architecture and its components.
• Deploying and managing containers within a Kubernetes cluster.
• Configuring networking, storage, and scaling for workloads.
• Troubleshooting common issues and adhering to best practices for cluster operations.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical application.
• Hands-on implementation within a live-lab environment.

Course Customization Options

• To arrange customized training for this course, please contact us directly.

Docker serves as a containerization platform designed to create portable, isolated, and secure deployment environments for AI inference services.

This instructor-led live training (available online or onsite) targets beginner to intermediate technical professionals seeking to develop secure, portable AI inference microservices that can be consistently deployed across local machines, servers, or cloud virtual machines.

Upon completing this workshop, participants will be able to:

• Construct lightweight inference containers for both local and cloud deployment.
• Secure containerized AI services using industry best practices.
• Implement portable microservice workflows to ensure consistent environments.
• Deploy AI inference endpoints across diverse infrastructures.

Course Format

• Guided lectures combined with practical demonstrations.
• Hands-on exercises to reinforce deployment and security techniques.
• Live-lab practice for building and running portable inference services.

Course Customization Options

• To tailor this training to your specific infrastructure or AI tooling stack, please contact us to arrange.

In this instructor-led, live training in Almada (onsite or remote), participants will learn how to deploy a collection of sample servers inside containers, then automate, scale, and manage their containerized servers within a Kubernetes cluster. The training goes on to more advanced topics, walking participants through the process of securing, networking and monitoring a Kubernetes cluster.

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

• Set up and run a Docker container.
• Deploy containerized databases and servers.
• Set up a Docker and Kubernetes cluster.
• Use Kubernetes to deploy and manage different environments under the same cluster.
• Secure, scale and monitor a Kubernetes cluster.

This instructor-led, live training in Almada (online or onsite) is aimed at intermediate-level professionals who wish to effectively deploy, manage, and scale containerized applications using Kubernetes.

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

• Understand the Kubernetes architecture and its components.
• Isolate resources effectively using Namespaces.
• Manage and customize workloads with Deployments, StatefulSets, and DaemonSets.
• Define computational resources using Requests and Limits.
• Work with Jobs and CronJobs for scheduled tasks.
• Understand Services and DNS within Kubernetes.
• Expose applications using Ingress.
• Manage ConfigMaps, Secrets, and Persistent Volumes.
• Scale and upgrade Kubernetes clusters using advanced strategies.
• Analyze and troubleshoot Kubernetes issues.
• Deploy resources efficiently using Helm Charts.

This instructor-led, live training in Almada (online or onsite) is aimed at intermediate-level to advanced-level developers, DevOps professionals, and architects who wish to design, deploy, and manage resilient applications using microservices, containers, and continuous integration/continuous deployment (CI/CD) pipelines.

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

• Understand and implement microservices architecture.
• Deploy and manage containerized applications with Docker and Kubernetes.
• Set up and optimize CI/CD pipelines for automated deployments.
• Apply best practices for security, monitoring, and observability.

Learn the fundamentals of containers, Kubernetes and OpenShift in a practical, hands-on training designed for developers, DevOps engineers and IT professionals. Participants will learn how to build containerized applications, deploy workloads, manage Kubernetes resources and use OpenShift to streamline modern application delivery in cloud and hybrid environments.

This instructor-led, live training in Almada (online or onsite) is designed for intermediate-level developers and DevOps engineers who want to incorporate Minikube into their development workflow.

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

• Set up and manage a local Kubernetes environment using Minikube.
• Understand how to deploy, manage, and debug applications on Minikube.
• Integrate Minikube into their continuous integration and deployment pipelines.
• Optimize their development process by leveraging Minikube's advanced features.
• Apply best practices for local Kubernetes development.

This instructor-led, live training in Almada (online or onsite) is designed for intermediate developers and DevOps engineers aiming to build, deploy, and manage microservices using Spring Cloud and Docker.

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

• Create microservices leveraging Spring Boot and Spring Cloud.
• Containerize applications using Docker and Docker Compose.
• Implement service discovery, API gateways, and inter-service communication.
• Monitor and secure microservices within production environments.
• Deploy and orchestrate microservices via Kubernetes.

This instructor-led, live training in Almada (online or onsite) is designed for beginner to intermediate software developers and DevOps professionals seeking to learn how to set up and manage a local Kubernetes environment using Minikube.

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

• Install and configure Minikube on their local machine.
• Understand the basic concepts and architecture of Kubernetes.
• Deploy and manage containers using kubectl and the Minikube dashboard.
• Set up persistent storage and networking solutions for Kubernetes.
• Utilize Minikube for developing, testing, and debugging applications.

This instructor-led live training (available online or onsite) is designed for engineers looking to automate, secure, and monitor containerized applications within a large-scale Kubernetes cluster.

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

• Utilize Kubernetes to deploy and manage diverse environments within a single cluster
• Secure, scale, and monitor a Kubernetes cluster effectively

Course Format

• Interactive lectures and discussions
• Numerous exercises and practical sessions
• Hands-on implementation in a live lab environment

Course Customization Options

• To arrange a customized training for this course, please get in touch with us.

Istio is an open-source service mesh that operates on Kubernetes to deliver secure, observable, and manageable connectivity between microservices. By utilising Istio’s Envoy-based sidecar proxies, teams can enforce policies, secure communications via mTLS, gain deep visibility into traffic patterns, and enhance reliability at scale.

This instructor-led, live training (available online or onsite) is designed for intermediate-level engineers looking to deploy, secure, and manage microservices applications using Istio on Kubernetes.

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

• Install and configure Istio on Kubernetes clusters.
• Understand and apply service mesh concepts, including traffic management, security, and observability.
• Deploy microservices applications within an Istio service mesh.
• Secure service-to-service communications using mutual TLS (mTLS) and Zero Trust principles.
• Monitor, trace, and troubleshoot microservices using Prometheus, Grafana, and Jaeger.
• Integrate Istio with Calico for advanced network policies and security.

Format of the Course

• Interactive lecture and discussion.
• Extensive exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

The rapid evolution of microservices and container technologies in recent years has fundamentally transformed how we design, develop, deploy, and operate software. Modern applications must be engineered for scalability, elasticity, resilience, and adaptability to change. To meet these new demands, contemporary architectures require a distinct set of patterns and best practices. This training explores methods to identify, comprehend, and adapt to these evolving requirements.

Target Audience
This course is designed for individuals who possess a foundational understanding of container technology and Kubernetes concepts but may lack practical, real-world experience. Drawing on use cases and lessons learned from actual projects, the goal is to inspire participants to create and manage superior cloud-native applications.

• Developers
• Operations Staff
• DevOps Professionals
• QA Engineers
• IT Project Managers

Course Format

• Interactive lectures and discussions
• Extensive exercises and practical application
• Hands-on implementation within a live laboratory environment

Course Customization Options

• To request a customized training session for this course, please contact us to arrange.

This instructor-led live training in Almada (online or onsite) is aimed at Kubernetes practitioners who wish to prepare for the CKS exam.

By the end of this training, participants will know how to secure Kubernetes environments and container-based applications throughout the different stages of an application's life cycle: build, deployment and runtime.

This instructor-led, live training in Almada (online or onsite) targets developers and DevOps engineers who wish to adopt a serverless approach for building enterprise applications in Kubernetes.

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

• Set up and configure the Kubernetes system to begin developing with a serverless architecture.
• Understand the concepts and principles foundational to serverless environments.
• Operate toolchains necessary for serverless development and integrate them with Kubernetes components.
• Practice skills in the Python programming language and apply them to implement serverless systems.
• Secure enterprise applications deployed through a serverless framework on Kubernetes.
• Utilize modern cloud computing methods in optimizing DevOps task processing workflows.

This instructor-led, live training in Almada (online or onsite) is designed for engineers who want to secure a Kubernetes cluster beyond the default security settings.

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

• Identify vulnerabilities exposed by a default Kubernetes installation.
• Prevent unauthenticated access to the Kubernetes API, database, and other services.
• Protect a Kubernetes cluster from accidental or malicious access.
• Develop a comprehensive security policy and set of best practices.

This instructor-led live training in Almada (online or on-site) is designed for engineers who wish to enhance their Docker proficiency, enabling the large-scale deployment of applications while maintaining strict control.

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

• Build their own Docker images.
• Deploy and manage a large number of Docker applications .
• Evaluate different container orchestration solutions and choose the most suitable one.
• Set up a continuous integration process for Docker applications.
• Integrate Docker applications with existing continuous tools integration processes.
• Secure their Docker applications.

Docker is an open-source platform designed to automate the process of building, shipping, and running applications within containers.

This instructor-led live training (available online or onsite) targets engineers who want to leverage Docker to deploy and manage software as containers rather than traditional standalone applications.

Upon completing this training, participants will be able to:

• Install and configure Docker.
• Grasp and implement software containerization principles.
• Manage Docker-based applications.
• Connect various Docker applications and systems via networking.
• Understand and manipulate Docker registries.

Format of the Course

• Interactive lectures and discussions.
• Extensive exercises and practical practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request customized training for this course, please contact us to make arrangements.
• To learn more about Docker, please visit: https://www.docker.com

This instructor-led, live training in Almada (online or onsite) is aimed at intermediate to advanced DevOps engineers and system administrators who wish to deploy and manage self-hosted Kubernetes clusters without cloud dependencies.

By the end of this training, participants will be able to: deploy production-ready Kubernetes clusters using kubeadm on bare-metal or virtual machines; configure high-availability control planes and etcd clusters; implement container networking and storage for self-managed environments; set up monitoring and observability using self-hosted solutions.

In this instructor-led live training held in Almada, participants will learn the basics of building microservices using Spring Cloud and Docker. Participants' knowledge will be tested through exercises and the incremental development of sample microservices.

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

• Understand the fundamentals of microservices.
• Use Docker to build containers for microservice applications.
• Build and deploy containerized microservices using Spring Cloud and Docker.
• Integrate microservices with discovery services and the Spring Cloud API Gateway.
• Use Docker Compose for end-to-end integration testing.

OpenShift is one of the leading Kubernetes-based platforms for deploying and managing containerized applications in cloud, hybrid and on-premises environments.

This hands-on training teaches participants how to install, administer and troubleshoot OpenShift clusters while applying security, networking and storage best practices. Through practical exercises, participants gain the skills needed to confidently manage production-ready OpenShift environments.

Learn how to develop, deploy, and manage containerized applications with OpenShift, one of the leading Kubernetes-based platforms for cloud-native development. This practical training covers application deployment, containers, networking, CI/CD, and DevOps workflows, providing participants with the skills to build and maintain modern applications in production environments.

This instructor-led, live training in Almada (online or on-site) is aimed at intermediate-level virtualization administrators who wish to use open-source platforms to migrate away from VMware.

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

• Install and configure KVM, oVirt, and Proxmox VE.
• Migrate virtual workloads from VMware.
• Implement high availability and disaster recovery.
• Optimize performance in open-source virtualization environments.