Containers and Virtual Machines (VMs) Training in Matosinhos

MLOps on Kubernetes is a framework for automating the training, validation, packaging, and deployment of machine learning models using containerized pipelines and GitOps workflows.

This instructor-led, live training (online or onsite) is aimed at intermediate-level practitioners who wish to build automated, scalable MLOps pipelines on Kubernetes.

After completing this training, participants will be equipped to:

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

Format of the Course

• 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.

Kubernetes is a scalable platform for deploying, serving, and managing machine learning models in production environments.

This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to operationalize ML models reliably on Kubernetes.

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

• Containerize and prepare ML models for efficient deployment.
• Serve models using modern inference frameworks.
• Optimize workloads with autoscaling, GPU configuration, and resource tuning.
• Implement model rollout strategies such as A/B testing and canary deployments.

Format of the Course

• Blended lecture, architectural analysis, and guided discussion.
• Extensive practical exercises with real-world deployment scenarios.
• Hands-on implementation using a live Kubernetes environment.

Course Customization Options

• If you need this course tailored to your environment or toolchain, please contact us to discuss customization options.

CI/CD for AI is a structured approach to automating model packaging, testing, containerization, and deployment using continuous integration and continuous delivery pipelines.

This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to automate end-to-end AI model delivery workflows using Docker and CI/CD platforms.

As the training concludes, participants will be able to:

• Create automated pipelines for building and testing AI model containers.
• Implement version control and reproducibility for model lifecycles.
• Integrate automated deployment strategies for AI services.
• Apply CI/CD best practices tailored to machine learning operations.

Format of the Course

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

Course Customization Options

• If your organization requires customized pipeline workflows or platform integrations, please contact us to tailor this course.

Hybrid AI deployment is the practice of running AI inference across cloud, on-premise, and edge environments using unified container-based workflows.

This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to design and deploy distributed AI inference systems across heterogeneous environments.

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

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

Format of the Course

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

Course Customization Options

• For tailored adjustments to align this course with your organization’s infrastructure or use cases, please contact us to customize the training.

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

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level IT professionals who wish to learn the core concepts and components of Kubernetes and use it to manage containerized applications at scale.

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

• Understand Kubernetes architecture and components.
• Deploy and manage containers in a Kubernetes cluster.
• Configure networking, storage, and scaling for workloads.
• Troubleshoot common issues and follow best practices for cluster operations.

Format of the Course

• Interactive lecture and discussion.
• Lots of 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.

In this instructor-led, live training in Matosinhos (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 Matosinhos (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 Matosinhos (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.

Docker is a containerization platform that enables consistent, portable, and reproducible environments for AI and machine learning workloads.

This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to package ML codebases, dependencies, and models using Docker for reliable development-to-production workflows.

After completing this course, participants will be able to:

• Build and manage Docker images tailored for AI and ML applications.
• Containerize machine learning pipelines, tools, and dependencies.
• Optimize Docker environments for performance and portability.
• Deploy containerized ML services across different runtime environments.

Format of the Course

• Concept demonstrations supported by guided discussion.
• Hands-on exercises focused on real-world containerization tasks.
• Practical implementation using live-lab Docker environments.

Course Customization Options

• To customize this training for your organizational environment, please contact us to arrange.

This instructor-led, live training in Matosinhos (online or onsite) is aimed at intermediate-level developers and DevOps engineers who wish to use Minikube as a part of their development workflow.

By the end 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 using Minikube's advanced features.
• Apply best practices for local Kubernetes development.

This instructor-led, live training in Matosinhos (online or onsite) is aimed at intermediate-level system administrators who wish to effectively deploy, manage, and troubleshoot Hyper-V environments.

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

• Understand virtualization concepts and configure Hyper-V.
• Create and manage virtual machines, VM storage, and networking.
• Utilize advanced Hyper-V features and secure Hyper-V environments.
• Monitor and optimize Hyper-V performance.

In this instructor-led, live training in Matosinhos (online or onsite), participants will learn how to set up and manage a production-scale container environment using Kubernetes on AKS. 

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

• Configure and manage Kubernetes on AKS.
• Deploy, manage and scale a Kubernetes cluster.
• Deploy containerized (Docker) applications on Azure.
• Migrate an existing Kubernetes environment from on-premise to AKS cloud.
• Integrate Kubernetes with third-party continuous integration (CI) software.
• Ensure high availability and disaster recovery in Kubernetes.

The objective of this practical course is to provide all the tools that address the practical aspects of Docker and Kubernetes, in order that the participants can count on the necessary skills for the proper management of the program.

This instructor-led, live training (online or onsite) is aimed at engineers wishing to automate, secure, and monitor containerized applications in a large-scale Kubernetes cluster.

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

• Use Kubernetes to deploy and manage different environments under the same cluster
• Secure, scale and monitor a Kubernetes cluster

Format of the Course

• Interactive lecture and discussion
• Lots of 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.

This instructor-led, live course in Matosinhos provides participants with an overview of Rancher and demonstrates through hands-on practice how to deploy and manage a Kubernetes cluster with Rancher.

Istio is an open-source service mesh that runs on Kubernetes to provide secure, observable, and manageable connectivity between microservices. By leveraging Istio’s Envoy-based sidecar proxies, teams can enforce policies, secure communications with mTLS, gain deep observability into traffic, and improve reliability at scale.

This instructor-led, live training (online or onsite) is aimed at intermediate-level engineers who wish 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 with mutual TLS (mTLS) and Zero Trust principles.
• Monitor, trace, and troubleshoot microservices with Prometheus, Grafana, and Jaeger.
• Integrate Istio with Calico for advanced network policies and security.

Format of the Course

• Interactive lecture and discussion.
• Lots of 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.

This instructor-led, live training in Matosinhos (online or onsite) is aimed at system administrators and developers who wish to use Prometheus to monitor Kubernetes clusters.

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

• Install and configure Prometheus.
• Monitor dynamic cloud environments.
• Gain better insight into their Kubernetes clusters.
• Set up alerts that are precise and insightful.

This instructor-led, live training in Matosinhos (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.

Apache Karaf is an OSGi based runtime for deploying containerized applications.

In this instructor-led, live training (onsite or remote), participants will learn how to set up an OSGi based project as they step through the deployment of a modular Java application using Apache Karaf.

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

• Install and configure Apache Karaf
• Understand the essential features of the OSGi runtime environment
• Develop a containerized application using the Apache Karaf run time environment

Audience

• Architects
• Developers

Format of the Course

• Part lecture, part discussion, exercises and heavy hands-on practice.

Note

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

In this instructor-led, live training in Matosinhos, we introduce the architecture, components and processes behind application virtualization and walk participants step-by-step through the deployment of App-V and App-V applications in a live lab environment. By the end of the course, participants will have knowledge and hands-on practice needed to install, configure, administer, and troubleshoot App-V as well as create, package and deploy their own App-V applications.

DC/OS (Distributed Cloud Operating System) is an open-source distributed operating system built upon the Apache Mesos project. Apache Mesos was built using the same principles as the Linux kernel, only at a different level of abstraction. The Mesos kernel runs on every machine and provides applications such as Hadoop, Spark, Kafka and Elasticsearch with API’s for resource management and scheduling across entire datacenter and cloud environments. Apache Mesos abstracts CPU, memory, storage, and other compute resources away from machines (physical or virtual), enabling fault-tolerant and elastic distributed systems to easily be built and run effectively.

DC/OS brings together Mesos and more than 30 applications into one coherent system, similar to how a Linux distribution packages the Linux kernel with applications to create a working operating system.

This course provides participants with an overview of Apache Mesos and DC/OS and demonstrates through hands-on practice how to deploy and manage a complete Data Center Operating System.

Audience

• System administrators
• DevOps engineers

Format of the course

• Part lecture, part discussion, heavy hands-on practice

This instructor-led, live training in Matosinhos (online or onsite) is aimed at engineers who wish to use Docker to deploy and manage software as containers instead of as traditional stand-alone software.

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

• Install and configure Docker.
• Understand and implement software containerization.
• Managing Docker based applications.
• Network different Docker applications and systems.
• Understand and edit Docker registries.

In this instructor-led, live training in Matosinhos, participants will learn the fundamentals of building microservices using Spring Cloud and Docker. Participant knowledge is put to the test through exercises and the step-by-step 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.

In this instructor-led, live training in Matosinhos (online or onsite), participants will learn how to install, configure, and manage OpenShift Container Platform v4.

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

• Undersand OCI (Open Containers Initiative) and its implications for using container enginees such as Docker in OpenShift.
• Understand the relationship between the different versions of OpenShift (OKP, OpenShift Container Platform, Red Hat OpenShift, etc.).
• Create, configure, manage, and troubleshoot an OpenShift cluster.
• Implement Openshift's self-manage capabilities to improve platform stability and consistency.
• Secure OpenShift Container Platform.
• Monitor and gather metrics.
• Manage storage.
• Upgrade an OpenShift Container Platform 3 cluster to OpenShift Container Platform 4.

This instructor-led, live training in Matosinhos (online or onsite) is aimed at DevOps engineers and developers who wish to use Podman to create, run, and manage containers and pods as a non-root user.

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

• Install and configure Podman in rootless mode.
• Build and run containers, container images, and pods.
• Manage container networking and lifecycle.
• Use Podman instead of Docker for creating and deploying containerized applications.

This instructor-led, live training in Matosinhos (online or onsite) is aimed at penetration testers who wish to penetrate test networks in Kali Linux using Python.

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

• Create Python programs to seek network vulnerabilities.
• Explore and use Kali web shells and shellcode in exploits.
• Apply various Kali tools for penetration testing.
• Exploit systems with Python.

This practical course contains topics addressed to system administrators, developers, embedded developers and ordinary desktop users (including managers) who would like to have an introduction to virtualization in general. After this course, administrators will be able to create private clouds for use in production, and developers will be able to test their code on multiple operating systems without needing a second PC or a non-x86 board.

We understand that the set of topics presented in this course covered a wide range of subjects that might not be applicable for all customers. When booking this course, please specify what you wish to achieve using QEMU or KVM, and we will customize it by reallocating time from less relevant topics to the ones that would be important for you.

This instructor-led, live training in Matosinhos (online or onsite) 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.

Microsoft Azure Infrastructure and Deployment