Containers and Virtual Machines (VMs) Training in Faro

Edge AI is a paradigm focused on running machine learning inference close to data sources to achieve low-latency, efficient, and scalable processing.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level practitioners who wish to deploy, orchestrate, and optimize AI workloads on Kubernetes-based edge environments.

By completing this course, participants will be able to:

• Set up lightweight Kubernetes distributions for edge deployments.
• Deploy AI inference workloads effectively across constrained edge nodes.
• Manage connectivity challenges and synchronization patterns.
• Optimize performance, storage, and networking for real-world edge scenarios.

Format of the Course

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

Course Customization Options

• To request a customized training tailored to your edge platform needs, please contact us to arrange.

Kubernetes is a container orchestration platform widely used for managing distributed applications at scale.

This instructor-led, live training (online or onsite) is aimed at advanced-level practitioners who wish to apply AI and machine learning techniques to optimize Kubernetes resource usage, scheduling decisions, and autoscaling strategies.

At the completion of this program, participants will be able to:

• Apply AI/ML models to improve workload scheduling decisions in Kubernetes.
• Use predictive analytics to optimize CPU, GPU, and memory allocation.
• Implement intelligent autoscaling using reinforcement learning and metric forecasting.
• Reduce infrastructure cost and latency through automated resource optimization.

Format of the Course

• Instructor-guided technical presentations and deep-dive discussions.
• Hands-on lab work using real Kubernetes clusters.
• Practical exercises applying AI models to real operational scenarios.

Course Customization Options

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

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.

Docker is a containerization platform used to build portable, isolated, and secure deployment environments for AI inference services.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level technical professionals who wish to build secure, portable AI inference microservices that can be deployed consistently across local machines, servers, or cloud VMs.

By the conclusion of this workshop, participants will be able to:

• Build lightweight inference containers for local and cloud deployment.
• Secure containerized AI services using best-practice techniques.
• Implement portable microservice workflows for consistent environments.
• Deploy AI inference endpoints across diverse infrastructures.

Format of the Course

• Guided lectures paired 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 infrastructure or AI tooling stack, please contact us to arrange.

This instructor-led, live training in Faro (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.

This instructor-led, live training in Faro (online or onsite) is aimed at advanced-level platform engineers and DevOps professionals who wish to master scaling applications using microservices and Kubernetes.

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

• Design and implement scalable microservices architectures.
• Deploy and manage applications on Kubernetes clusters.
• Utilize Helm charts for efficient service deployment.
• Monitor and maintain the health of microservices in production.
• Apply best practices for security and compliance in a Kubernetes environment.

DO180 is an introduction to containers, Kubernetes fundamentals, and Red Hat OpenShift platform concepts focused on hands-on skills.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level technical professionals who wish to learn container workflows, Kubernetes primitives, and how to deploy and operate applications on OpenShift.

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

• Build and manage container images and registries with best practices for reproducibility and security.
• Deploy and manage Kubernetes objects such as pods, deployments, and services in OpenShift.
• Use OpenShift features including routes, buildconfigs, and the web console to streamline application delivery.
• Implement persistent storage, configuration management, and secrets handling for stateful workloads.
• Apply basic security, RBAC, and monitoring practices to maintain healthy clusters and applications.

Format of the Course

• Interactive lecture and discussion.
• Hands-on labs in a live OpenShift environment every day.
• Scenario-driven exercises and troubleshooting workshops.

Course Customization Options

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

Docker is a containerization platform used to build reproducible, portable, and scalable environments for ML systems.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level technical professionals who wish to containerize and operationalize complete ML pipelines using Docker.

Upon completion of 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 supporting tools.
• Implement versioning, reproducibility, and CI/CD for ML components.
• Deploy, monitor, and scale ML services in containerized environments.

Format of the Course

• Interactive lectures supported by practical demonstrations.
• Hands-on exercises focused on building real ML pipeline components.
• Live-lab implementation for end-to-end containerized workflows.

Course Customization Options

• For customized training aligned with specific ML infrastructure needs, please contact us to discuss options.

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 Faro (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.

This instructor-led, live training in Faro (online or onsite) is aimed at beginner to intermediate-level developers, system administrators, and DevOps engineers who wish to understand Kubernetes and cloud-native principles.

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

• Set up a development environment that includes Kubernetes.
• Create a microservices application using Kubernetes.
• Deploy, manage, and scale applications in Kubernetes.
• Debug common issues in Kubernetes environments.

In this instructor-led, live training in Faro (onsite or remote), participants will learn how to create and manage Docker containers, then deploy a sample application inside a container. Participants will also learn how to automate, scale, and manage their containerized applications within a Kubernetes cluster. Finally, the training goes on to more advanced topics, walking participants through the process of securing, scaling and monitoring a Kubernetes cluster.

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

• Set up and run a Docker container.
• Deploy a containerized server and web application.
• Build and manage Docker images.
• Set up a Docker and Kubernetes cluster.
• Use Kubernetes to deploy and manage a clustered web application.
• Secure, scale and monitor a Kubernetes cluster.

This instructor-led, live training in Faro (online or onsite) is aimed at engineers who wish to evaluate, select and deploy the most appropriate Kubernetes solution for their specific situation.

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

• Install and configure a Kubernetes cluster on premise and in the cloud.
• Evaluate different cloud-hosted Kubernetes solutions and understand the advantages over an self-hosted approach.
• Compare different tools for testing, managing, and monitoring a Kubernetes cluster.

This instructor-led, live training in Faro (online or onsite) is aimed at engineers who wish to optimize networking for Kubernetes clusters.

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

• Install and configure Calico.
• Understand how Calico differs from traditional overlay networks.
• Understand how Calico combines internet routing protocols with consensus-based data stores.
• Use Calico to create a container networking solution for Kubernetes clusters.
• Use Calico to provide network policy for Kubernetes.

In this instructor-led, live training in Faro, participants will learn how to set up and manage a production-scale container environment using Kubernetes on Google Cloud. 

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

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

This instructor-led, live training in Faro (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 Faro (online or onsite) is aimed at engineers who wish to advance their knowledge of Docker so as to deploy applications at a larger scale while maintaining control.

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

• Build their own Docker images.
• Deploy and manager 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.

In this instructor-led, live training in Faro, we cover advanced techniques and troubleshooting for Microsoft Application Virtualization (App-V), especially in the area of sequencing and packaging.

By the end of the course, participants will have a deep understanding of App-V and be able to sequence, troubleshoot and resolve complex issues.

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

Docker is a platform for developers and sysadmins to maintain distributed applications. It consists of a runtime to run containers and a service for sharing containers.

With docker the same app can run unchanged on laptops, dedicated servers and virtual servers.

This course teaches the basic usage of Docker, useful both for developers and system administrators. The course includes a lot of hands on exercises and the participants will practice in their own Docker environment and build their own Docker images during the 2 days.

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 Faro (online or onsite) is aimed at DevOps engineers and developers who wish to use Java and Kubernetes (K8s) to create, build, test, debug and deploy high-performance and scalable applications.

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

• Set up the necessary development environment to build Java applications.
• Understand the features and architecture of Kubernetes.
• Learn about the key concepts and tools for DevOps.
• Get a refresher on Java programming fundamentals.
• Containerize Java microservices using Docker.
• Build, scale, and deploy Java applications on Kubernetes.

In this instructor-led, live training in Faro, 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.

This instructor-led, live training in Faro (online or onsite) is aimed at DevOps engineers and developers who wish to deploy and manage OpenStack services on cloud infrastructures using Kubernetes.

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

• Deploy, configure, and manage MOS and its components.
• Access OpenStack APIs.
• Perform common OpenStack operations tasks (backup, restore, etc.).
• Upgrade OpenStack deployments.

This instructor-led, live training in Faro (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 Faro (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 Faro (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