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Duration 4 Days 24 CPD hours This course is intended for This course is intended for: Developers Solutions Architects Data Engineers Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon SageMaker Overview In this course, you will learn to: Select and justify the appropriate ML approach for a given business problem Use the ML pipeline to solve a specific business problem Train, evaluate, deploy, and tune an ML model using Amazon SageMaker Describe some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS Apply machine learning to a real-life business problem after the course is complete This course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem. Module 0: Introduction Pre-assessment Module 1: Introduction to Machine Learning and the ML Pipeline Overview of machine learning, including use cases, types of machine learning, and key concepts Overview of the ML pipeline Introduction to course projects and approach Module 2: Introduction to Amazon SageMaker Introduction to Amazon SageMaker Demo: Amazon SageMaker and Jupyter notebooks Hands-on: Amazon SageMaker and Jupyter notebooks Module 3: Problem Formulation Overview of problem formulation and deciding if ML is the right solution Converting a business problem into an ML problem Demo: Amazon SageMaker Ground Truth Hands-on: Amazon SageMaker Ground Truth Practice problem formulation Formulate problems for projects Module 4: Preprocessing Overview of data collection and integration, and techniques for data preprocessing and visualization Practice preprocessing Preprocess project data Class discussion about projects Module 5: Model Training Choosing the right algorithm Formatting and splitting your data for training Loss functions and gradient descent for improving your model Demo: Create a training job in Amazon SageMaker Module 6: Model Evaluation How to evaluate classification models How to evaluate regression models Practice model training and evaluation Train and evaluate project models Initial project presentations Module 7: Feature Engineering and Model Tuning Feature extraction, selection, creation, and transformation Hyperparameter tuning Demo: SageMaker hyperparameter optimization Practice feature engineering and model tuning Apply feature engineering and model tuning to projects Final project presentations Module 8: Deployment How to deploy, inference, and monitor your model on Amazon SageMaker Deploying ML at the edge Demo: Creating an Amazon SageMaker endpoint Post-assessment Course wrap-up Additional course details: Nexus Humans The Machine Learning Pipeline on AWS training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the The Machine Learning Pipeline on AWS course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 1 Days 6 CPD hours This course is intended for This class is intended for the following: Data analysts, Data scientists, Business analysts getting started with Google Cloud Platform. Individuals responsible for designing pipelines and architectures for data processing, creating and maintaining machine learning and statistical models, querying datasets, visualizing query results and creating reports. Executives and IT decision makers evaluating Google Cloud Platform for use by data scientists. Overview This course teaches students the following skills:Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform.Use Cloud SQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform.Employ BigQuery and Cloud Datalab to carry out interactive data analysis.Train and use a neural network using TensorFlow.Employ ML APIs.Choose between different data processing products on the Google Cloud Platform. This course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities. Introducing Google Cloud Platform Google Platform Fundamentals Overview. Google Cloud Platform Big Data Products. Compute and Storage Fundamentals CPUs on demand (Compute Engine). A global filesystem (Cloud Storage). CloudShell. Lab: Set up a Ingest-Transform-Publish data processing pipeline. Data Analytics on the Cloud Stepping-stones to the cloud. Cloud SQL: your SQL database on the cloud. Lab: Importing data into CloudSQL and running queries. Spark on Dataproc. Lab: Machine Learning Recommendations with Spark on Dataproc. Scaling Data Analysis Fast random access. Datalab. BigQuery. Lab: Build machine learning dataset. Machine Learning Machine Learning with TensorFlow. Lab: Carry out ML with TensorFlow Pre-built models for common needs. Lab: Employ ML APIs. Data Processing Architectures Message-oriented architectures with Pub/Sub. Creating pipelines with Dataflow. Reference architecture for real-time and batch data processing. Summary Why GCP? Where to go from here Additional Resources Additional course details: Nexus Humans Google Cloud Platform Big Data and Machine Learning Fundamentals training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Google Cloud Platform Big Data and Machine Learning Fundamentals course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 1 Days 6 CPD hours This course is intended for This course is designed for data scientists with experience of Python who need to learn how to apply their data science and machine learning skills on Azure Databricks. Overview After completing this course, you will be able to: Provision an Azure Databricks workspace and cluster Use Azure Databricks to train a machine learning model Use MLflow to track experiments and manage machine learning models Integrate Azure Databricks with Azure Machine Learning Azure Databricks is a cloud-scale platform for data analytics and machine learning. In this course, students will learn how to use Azure Databricks to explore, prepare, and model data; and integrate Databricks machine learning processes with Azure Machine Learning. Introduction to Azure Databricks Getting Started with Azure Databricks Working with Data in Azure Databricks Training and Evaluating Machine Learning Models Preparing Data for Machine Learning Training a Machine Learning Model Managing Experiments and Models Using MLflow to Track Experiments Managing Models Managing Experiments and Models Using MLflow to Track Experiments Managing Models Integrating Azure Databricks and Azure Machine Learning Tracking Experiments with Azure Machine Learning Deploying Models

Duration 1 Days 6 CPD hours This course is intended for Software Engineers Overview The objective of this course is to learn the key language concepts to machine learning, Spark MLlib, and Spark ML. This course will teach you the key language concepts to machine learning, Spark MLlib, and Spark ML. The course includes coverage of collaborative filtering, clustering, classification, algorithms, and data volume. This course will teach you the key language concepts to machine learning, Spark MLlib, and Spark ML. The course includes coverage of collaborative filtering, clustering, classification, algorithms, and data volume.

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Blue CSCS Card NVQ Level 2 Plant This qualification provides you with the opportunity to showcase their knowledge, skills and understanding in their chosen specialism. You will have the relevant experience in one of the specific areas. You will be operating on one of the following machines: Forward Tipping Dumper Ride on Roller Excavator Telehandler Induction As soon as you register you will be given a dedicated assessor. They will arrange an induction and together with your assessor, you will get to decide on the pathway which best proves your competency. The induction is used to plan out how you will gather the relevant evidence to complete the course. During the course The assessor will work with you to build a portfolio of evidence that allows you to showcase your knowledge, skills and experience. The assessor will also regularly review and provide you with feedback. This will allow you to keep on track to progress quickly. You will be assessed through various methods such as observations, written questions, evidence generated from the workplace, professional discussion, and witness testimonials. On completion Once all feedback has been agreed, the Internal Quality Assurer will review your portfolio and in agreement with your assessor the certificate will be applied for. To download our PDF for this course then please click here.

Duration 5 Days 30 CPD hours This course is intended for The skills covered in this course converge on four areas-software development, IT operations, applied math and statistics, and business analysis. Target students for this course should be looking to build upon their knowledge of the data science process so that they can apply AI systems, particularly machine learning models, to business problems. So, the target student is likely a data science practitioner, software developer, or business analyst looking to expand their knowledge of machine learning algorithms and how they can help create intelligent decisionmaking products that bring value to the business. A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming. This course is also designed to assist students in preparing for the CertNexus Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) certification Overview In this course, you will develop AI solutions for business problems. You will: Solve a given business problem using AI and ML. Prepare data for use in machine learning. Train, evaluate, and tune a machine learning model. Build linear regression models. Build forecasting models. Build classification models using logistic regression and k -nearest neighbor. Build clustering models. Build classification and regression models using decision trees and random forests. Build classification and regression models using support-vector machines (SVMs). Build artificial neural networks for deep learning. Put machine learning models into operation using automated processes. Maintain machine learning pipelines and models while they are in production Artificial intelligence (AI) and machine learning (ML) have become essential parts of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, all while following a methodical workflow for developing data-driven solutions. Solving Business Problems Using AI and ML Topic A: Identify AI and ML Solutions for Business Problems Topic B: Formulate a Machine Learning Problem Topic C: Select Approaches to Machine Learning Preparing Data Topic A: Collect Data Topic B: Transform Data Topic C: Engineer Features Topic D: Work with Unstructured Data Training, Evaluating, and Tuning a Machine Learning Model Topic A: Train a Machine Learning Model Topic B: Evaluate and Tune a Machine Learning Model Building Linear Regression Models Topic A: Build Regression Models Using Linear Algebra Topic B: Build Regularized Linear Regression Models Topic C: Build Iterative Linear Regression Models Building Forecasting Models Topic A: Build Univariate Time Series Models Topic B: Build Multivariate Time Series Models Building Classification Models Using Logistic Regression and k-Nearest Neighbor Topic A: Train Binary Classification Models Using Logistic Regression Topic B: Train Binary Classification Models Using k-Nearest Neighbor Topic C: Train Multi-Class Classification Models Topic D: Evaluate Classification Models Topic E: Tune Classification Models Building Clustering Models Topic A: Build k-Means Clustering Models Topic B: Build Hierarchical Clustering Models Building Decision Trees and Random Forests Topic A: Build Decision Tree Models Topic B: Build Random Forest Models Building Support-Vector Machines Topic A: Build SVM Models for Classification Topic B: Build SVM Models for Regression Building Artificial Neural Networks Topic A: Build Multi-Layer Perceptrons (MLP) Topic B: Build Convolutional Neural Networks (CNN) Topic C: Build Recurrent Neural Networks (RNN) Operationalizing Machine Learning Models Topic A: Deploy Machine Learning Models Topic B: Automate the Machine Learning Process with MLOps Topic C: Integrate Models into Machine Learning Systems Maintaining Machine Learning Operations Topic A: Secure Machine Learning Pipelines Topic B: Maintain Models in Production

Duration 5 Days 30 CPD hours This course is intended for System administrators System engineers Overview By the end of the course, you should be able to meet the following objectives: Describe the software-defined data center (SDDC) Explain the vSphere components and their function in the infrastructure Install and configure VMware ESXi™ hosts Deploy and configure VMware vCenter Server Appliance™ Use VMware vSphere Client™ to manage the vCenter Server inventory and the vCenter Server configuration Manage, monitor, back up, and protect vCenter Server Appliance Create virtual networks with vSphere standard switches Describe the storage technologies supported by vSphere Configure virtual storage using iSCSI and NFS storage Create and manage VMware vSphere VMFS datastores Use the vSphere Client to create virtual machines, templates, clones, and snapshots Create a content library and deploy virtual machines from templates in the library Manage virtual machine resource use and manage resource pools Migrate virtual machines with VMware vSphere vMotion and VMware vSphere Storage vMotion Create and manage a vSphere cluster that is enabled with VMware vSphere High Availability and VMware vSphere Distributed Resource Scheduler™ Create virtual networks with VMware vSphere Distributed Switch™ and enable distributed switch features Discuss solutions for managing the vSphere life cycle Use VMware vSphere Lifecycle Manager™ to apply patches and perform upgrades to ESXi hosts and virtual machines Use host profiles to manage ESXi configuration compliance Describe how vSphere storage APIs help storage systems integrate with vSphere Configure and use virtual machine storage policies Discuss the purpose and capabilities of VMware vSphere with Kubernetes and how it fits into the VMware Tanzu™ portfolio This five-day, intensive course takes you from introductory to advanced VMware vSphere© 7 management skills. Building on the installation and configuration content from our best-selling course, you will also develop advanced skills needed to manage and maintain a highly available and scalable virtual infrastructure. Through a mix of lecture and hands-on labs, you will install, configure and manage vSphere 7. You will explore the features that build a foundation for a truly scalable infrastructure and discuss when and where these features have the greatest effect. This course prepares you to administer a vSphere infrastructure for an organization of any size using vSphere 7, which includes VMware ESXi? 7 and VMware vCenter Server© 7. Course Introduction Introductions and course logistics Course objectives Introduction to vSphere and the Software-Defined Data Center Explain basic virtualization concepts Describe how vSphere fits into the software-defined data center and the cloud infrastructure Explain how vSphere interacts with CPUs, memory, networks, and storage Recognize the user interfaces for accessing the vCenter Server system and ESXi hosts Use VMware Host Client? to access and manage ESXi host Virtual Machines Create and remove a virtual machine Provision a virtual machine with virtual devices Identify the files that make up a virtual machine Explain the importance of VMware Tools vCenter Server Describe the vCenter Server architecture Discuss how ESXi hosts communicate with vCenter Server Deploy and configure vCenter Server Appliance Use the vSphere Client to manage the vCenter Server inventory Add data center, organizational objects, and hosts to vCenter Server Use roles and permissions to enable users to access objects in the vCenter Server inventory Back up vCenter Server Appliance Monitor vCenter Server tasks, events, and appliance health Use vCenter Server High Availability to protect a vCenter Server Appliance Configuring and Managing Virtual Networks Create and manage standard switches Describe the virtual switch connection types Configure virtual switch security, traffic-shaping and load-balancing policies Compare vSphere distributed switches and standard switches Configuring and Managing Virtual Storage Identify storage protocols and storage device types Discuss ESXi hosts using iSCSI, NFS, and Fibre Channel storage Create and manage VMFS and NFS datastores Explain how multipathing works with iSCSI, NFS, and Fibre Channel storage Deploy virtual machines on a VMware vSAN? datastore Virtual Machine Management Use templates and cloning to deploy new virtual machines Modify and manage virtual machines Create a content library and deploy virtual machines from templates in the library Dynamically increase the size of a virtual disk Use customization specification files to customize a new virtual machine Perform vSphere vMotion and vSphere Storage vMotion migrations Create and manage virtual machine snapshots Examine the features and functions of VMware vSphere© Replication? Resource Management and Monitoring Discuss CPU and memory concepts in a virtualized environment Describe what over commitment of a resource means Describe methods for optimizing CPU and memory usage Use various tools to monitor resource use Create and use alarms to report certain conditions or events vSphere Clusters Describe options for making a vSphere environment highly available Explain the vSphere HA architecture Configure and manage a vSphere HA cluster Examine the features and functions of VMware vSphere© Fault Tolerance Configure a vSphere cluster using ESXi Cluster Quickstart Describe the functions of a vSphere DRS cluster Create a vSphere DRS cluster Network Scalability Configure and manage vSphere distributed switches Describe how VMware vSphere© Network I/O Control enhances performance Explain distributed switch features such as port mirroring and NetFlow vSphere Lifecycle Management Describe how VMware vSphere© Lifecycle Manager? works Use vSphere Lifecycle Manager to update ESXi hosts in a cluster Host and Management Scalability Use host profiles to manage ESXi configuration compliance Create and manage resource pools in a cluster Storage Scalability Explain why VMware vSphere© VMFS is a highperformance, scalable file system Explain VMware vSphere© Storage APIs - Array Integration, VMware vSphere© API for Storage Awareness?, and vSphere APIs for I/O Filtering Configure and assign virtual machine storage policies Create VMware vSAN? storage policies Configure VMware vSphere© Storage DRS? and VMware vSphere© Storage I/O Control Discuss vSphere support for NVMe and iSER Introduction to vSphere with Kubernetes Differentiate between containers and virtual machines Identify the parts of a container system Recognize the basic architecture of Kubernetes Describe a basic Kubernetes workflow Describe the purpose of vSphere with Kubernetes and how it fits into the VMware Tanzu portfolio Explain the vSphere with Kubernetes supervisor cluster Describe the Tanzu Kubernetes Grid service Additional course details:Notes Delivery by TDSynex, Exit Certified and New Horizons an VMware Authorised Training Centre (VATC) Nexus Humans VMware vSphere: Fast Track [v7.0] training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the VMware vSphere: Fast Track [v7.0] course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 4 Days 24 CPD hours This course is intended for The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview Overview of data science and machine learning at scale Overview of the Hadoop ecosystem Working with HDFS data and Hive tables using Hue Introduction to Cloudera Data Science Workbench Overview of Apache Spark 2 Reading and writing data Inspecting data quality Cleansing and transforming data Summarizing and grouping data Combining, splitting, and reshaping data Exploring data Configuring, monitoring, and troubleshooting Spark applications Overview of machine learning in Spark MLlib Extracting, transforming, and selecting features Building and evaluating regression models Building and evaluating classification models Building and evaluating clustering models Cross-validating models and tuning hyperparameters Building machine learning pipelines Deploying machine learning models Spark, Spark SQL, and Spark MLlib PySpark and sparklyr Cloudera Data Science Workbench (CDSW) Hue This workshop covers data science and machine learning workflows at scale using Apache Spark 2 and other key components of the Hadoop ecosystem. The workshop emphasizes the use of data science and machine learning methods to address real-world business challenges. Using scenarios and datasets from a fictional technology company, students discover insights to support critical business decisions and develop data products to transform the business. The material is presented through a sequence of brief lectures, interactive demonstrations, extensive hands-on exercises, and discussions. The Apache Spark demonstrations and exercises are conducted in Python (with PySpark) and R (with sparklyr) using the Cloudera Data Science Workbench (CDSW) environment. The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview of data science and machine learning at scaleOverview of the Hadoop ecosystemWorking with HDFS data and Hive tables using HueIntroduction to Cloudera Data Science WorkbenchOverview of Apache Spark 2Reading and writing dataInspecting data qualityCleansing and transforming dataSummarizing and grouping dataCombining, splitting, and reshaping dataExploring dataConfiguring, monitoring, and troubleshooting Spark applicationsOverview of machine learning in Spark MLlibExtracting, transforming, and selecting featuresBuilding and evauating regression modelsBuilding and evaluating classification modelsBuilding and evaluating clustering modelsCross-validating models and tuning hyperparametersBuilding machine learning pipelinesDeploying machine learning models Additional course details: Nexus Humans Cloudera Data Scientist Training training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Cloudera Data Scientist Training course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 5 Days 30 CPD hours This course is intended for Linux system administrators, virtualization administrators, and hybrid infrastructure engineers interested in deploying large-scale virtualization solutions and managing virtual servers in their datacenters, based on the Red Hat Virtualization open virtualization management platform. Overview Configure Red Hat Virtualization Configure networking and storage for use with Red Hat Virtualization Manage user accounts and access to the Red Hat Virtualization environment Install and manage virtual machines in Red Hat Virtualization Use templates for rapid virtual machine deployment Manage virtual machine snapshots and images Migrate virtual machines and explore high-availability options Deploy, configure, manage, and migrate virtual environments Red Hat Virtualization (RH318) teaches you the skills needed to deploy, administer, and operate virtual machines in your organization using Red Hat© Virtualization. Through numerous hands-on exercises, you will demonstrate the ability to deploy and configure the Red Hat Virtualization infrastructure and use it to provision and manage virtual machines. This offering also prepares you for the Red Hat Certified Specialist in Virtualization exam. This course is based on Red Hat Enterprise Virtualization 4.3 and Red Hat Enterprise Linux© 7.6 and 8, as well as Red Hat Hyperconverged Infrastructure for Virtualization 1.6. 1 - Red Hat Virtualization overview Explain the purpose and architecture of Red Hat Virtualization. 2 - Install and configure Red Hat Virtualization Install a minimal Red Hat Virtualization environment and use it to create a virtual machine. 3 - Create and manage datacenters and clusters Organize hypervisors into groups using datacenters and clusters. 4 - Manage user accounts and roles Configure user accounts using a central directory service, then use roles to assign access to resources based on job responsibilities. 5 - Adding physical hosts Add additional Red Hat Virtualization hosts automatically, and move and remove hosts from datacenters as needed. 6 - Scale Red Hat Virtualization infrastructure Add Red Hat Virtualization hosts automatically, configure Red Hat Enterprise Linux hosts when appropriate, and move and remove hosts from data centers as needed. 7 - Manage Red Hat Virtualization networks Separate network traffic into multiple networks on one or more interfaces to improve the performance and security of Red Hat Virtualization. 8 - Manage Red Hat Virtualization storage Create and manage data and ISO storage domains. 9 - Deploy and manage virtual machines Operate virtual machines in the Red Hat Virtualization environment. 10 - Migrate virtual machines Migrate and control automatic migration of virtual machines. 11 - Manage virtual machine images Manage virtual machine snapshots and disk images. 12 - Automating virtual machine deployment Automate deployment of virtual machines by using templates and cloud-init. 13 - Back up and upgrade Red Hat Virtualization Back up, restore, and upgrade the software in a Red Hat Virtualization environment. 14 - Explore high-availability practices Explain procedures to improve the resilience and reliability of Red Hat Virtualization by removing single points of failure and implementing high-availability features. 15 - Perform comprehensive review Demonstrate skills learned in this course by installing and configuring Red Hat Virtualization; using the platform to create and manage virtual machines; and backing up and updating components of Red Hat Virtualization. Additional course details: Nexus Humans Red Hat Virtualization (RH318) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Red Hat Virtualization (RH318) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.