Onsite Training

All Instructors, Enterprise Architects, and Solution Architects are IBM or Red Hat Certified

AI Instructor Lead Training

Learn the Basics of Machine Learning with IBM Watson Studio

This course introduces a case study, data set, machine learning concepts, and developing a machine learning model with Watson Studio.

Initially, you will be introduced to the case study and the challenges company facing, and the company data set. Next, you will be introduced to supervised, unsupervised learning, deep and reinforcement learning algorithms. Finally, you will develop a supervised machine learning model IBM Watson Studio with the dataset provided using Python.

Course details

Audience
AI Specialists who want to learn machine learning algorithms

Prerequisites
• Some experience in Python
• Some experience in Jupyter notebook
• Some experience in Watson Studio or completion of Watson Studio Primer
• A Watson Studio Lite plan

Objectives
• Describe the use case and the data set
• Distinguish between supervised and unsupervised machine learning
• Define deep learning and reinforcement learning
• Demonstrate the basic functions of Watson Studio for machine learning

Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2)

Course details

Audience

  • Data scientists
  • Business analysts
  • Clients who want to learn about machine learning models

Prerequisites

  • Knowledge of your business requirements

Objectives

  • Introduction to machine learning models
  • Taxonomy of machine learning models
  • Identify measurement levels
  • Taxonomy of supervised models
  • Build and apply models in IBM SPSS Modeler 

Supervised models: Decision trees - CHAID

  • CHAID basics for categorical targets
  • Include categorical and continuous predictors
  • CHAID basics for continuous targets
  • Treatment of missing values 

Supervised models: Decision trees - C&R Tree 

  • C&R Tree basics for categorical targets
  • Include categorical and continuous predictors
  • C&R Tree basics for continuous targets
  • Treatment of missing values 
  • Evaluation measures for supervised models
  • Evaluation measures for categorical targets
  • Evaluation measures for continuous targets  

Supervised models: Statistical models for continuous targets - Linear regression

  • Linear regression basics
  • Include categorical predictors
  • Treatment of missing values 
  • Supervised models: Statistical models for categorical targets - Logistic regression
  • Logistic regression basics
  • Include categorical predictors
  • Treatment of missing values 

Association models: Sequence detection

  • Sequence detection basics
  • Treatment of missing values 

Supervised models: Black box models - Neural networks

  • Neural network basics
  • Include categorical and continuous predictors
  • Treatment of missing values   

Supervised models: 

  • Black box models - Ensemble models
  • Ensemble models basics
  • Improve accuracy and generalizability by boosting and bagging
  • Ensemble the best models 

Unsupervised models: K-Means and Kohonen

  • K-Means basics
  • Include categorical inputs in K-Means
  • Treatment of missing values in K-Means
  • Kohonen networks basics
  • Treatment of missing values in Kohonen 

Unsupervised models: TwoStep and Anomaly detection

  • TwoStep basics
  • TwoStep assumptions
  • Find the best segmentation model automatically
  • Anomaly detection basics
  • Treatment of missing values  

 

Association models: Apriori

  • Apriori basics
  • Evaluation measures
  • Treatment of missing values 
  • Preparing data for modeling
  • Examine the quality of the data 
  • Select important predictors 
  • Balance the data

Building Intelligent Virtual Agents with IBM watsonx Assistant

Building Intelligent Virtual Agents with IBM watsonx Assistant aims to equip nontechnical business users with the skills and experience to build and launch their own intelligent virtual agent with IBM watsonx Assistant. The course walks users through the process of planning, and then building, a virtual agent. You learn to create basic and advanced conversational flows that fulfill users’ requests, leverage out of the box artificial intelligence features, and tie into analytics dashboards for monitoring and troubleshooting. The lab environment for this course uses IBM Cloud.

Basics of Intelligent Document Processing with IBM Watson Discovery

This course introduces you to common NLP tasks, Smart Document Understanding, customization of the query results, teach the domain language to Watson Discovery to improve the relevancy and accuracy of the results.

Watson Discovery is a cognitive search and content analytics engine that you can add to applications to identify patterns, trends, and actionable insights to drive better decision-making.

Creating Voice Interfaces with Watson Speech to Text and Text to Speech

An emerging trend in AI is the availability of technologies that add speech capabilities by enabling fast and accurate speech transcription in multiple languages for various use cases, including but not limited to customer self-service, agent assistance and speech analytics. This course takes the learners through applying IBM Watson Speech to Text and Text to Speech technology for their unique use case. The course starts by explaining at a high level some common business use cases for Watson Speech Services and the underlying science behind the technology of Watson Speech to Text and Text to Speech as developed by IBM, leverage the API methods for calling Speech services, customize, and deploy speech prototypes to suit your unique domain language and finally the course will end by integrating voice capabilities to an existing agent Watson Assistant using Watson Speech to Text and Text to Speech.

Overview of IBM Cognos Analytics (v11.2)

This course provides participants with a high-level overview of the IBM Cognos Analytics suite of products and their underlying architecture. They will examine each component as it relates to an Analytics solution. Participants will be shown a range of resources to provide additional information on each product.

Quickly Build and Train Machine Learning Models with IBM AutoAI

Course Overview
Quickly build and train machine learning models with Watson AutoAI aims to familiarize data science and analytics professionals with the fundamentals of theWatson Studio’s AutoAI tool. The course walks users through the process of creatingIBM Cloud projects, and building and evaluating AutoAI experiments for various supervised machine learning use cases.

The lab environment for this course uses IBM Cloud.

Course details
Audience

This course is designed for data analysts, data scientists, and machine learning specialists.

Prerequisites
• IBM Cloud access
• Knowledge of supervised machine learning use cases
• Knowledge of Python code in notebook environments
• Basic knowledge of machine learning evaluation metrics

Objectives
• Set up an IBM Cloud Account and project and add and manage associated resources
• Identify potential machine learning use cases applicable to AutoAI
• Differentiate problem types relevant for AutoAI experiments (Classification, Regression, Time Series)
• Configure settings for various AutoAI experiments
• Evaluate pipelines and models that are produced by AutoAI experiments
• Recognize deployment strategies for AutoAI models

IBM SPSS Statistics Essentials

This course guides students through the fundamentals of using IBM SPSS Statistics for typical data analysis. Students will learn the basics of reading data, data definition, data modification, data analysis, and presentation of analytical results. In addition to the fundamentals, students will learn shortcuts that will help them save time. This course uses the IBM SPSS Statistics Base; one section presents an add-on module, IBM SPSS Custom Tables.

Creating, Testing, and Deploying Machine Learning Models with IBM Watson Studio V4.8

The focus of this course is on the tools and services available in IBM Watson Studio that can be used to build, test, and deploy machine learning models on Cloud Pak for Data V4.8. It takes the data scientist or business analyst on a journey from the creation of several machine learning models to its deployment and testing. Various tools and services, as well as programming and graphical user interfaces, are used in the process. The course ends with the sharing of assets on GitHub, and a brief discussion on governance and stewardship.

UseIBM Watson APIs to Get Structured Data from Unstructured Text and Voice

In this course, the learner is guided through a realistic data science project. The project focuses on extracting business insights from large amounts of unstructured voice data, by using IBM Watson APIs. Starting from voice recorded customer reviews, the data are transcribed by using Watson Speech to Text and then are analyzed for emotion and keywords by using Watson Discovery. The structured results are stored in a tabular form and used to extract business insights in Watson Studio.

The course focuses on hands-on coding exercises that provide an opportunity to work with Watson Studio, Watson Discovery, and Watson Speech to Text. The exercises guide the learner on importing data and manipulating in a Watson Studio project, activate instances of installed services, and authenticate API connections by using Jupyter Notebooks. The exercises also introduce how to configure Watson Speech to Text and create custom language models, work with Watson Discovery, and introduce enrichments. Also, every exercise demonstrates suitable data manipulation that results in saved structured data.

The three hands-on exercises are included in this course, each focusing on a Jupyter Notebook and correspond to the three modules of the course. The necessary data, notebooks, and services are all available in the corresponding lab environment.

Cybersecurity Instructor Lead Training

IBM Security Verify Access Foundations

IBM Security Verify Access* helps you simplify your users' access while more securely adopting web, mobile, IoT, and cloud technologies. It can be deployed on premises, in a virtual or hardware appliance, or containerized with Docker. Verify Access also directly connects with Verify SaaS for a modernized, hybrid IAM approach to enable your organization's migration to identity as a service (IDaaS) at a comfortable pace. Using the skills taught in this course, you learn how to run Verify Access via Docker, configure authentication and authorization mechanisms, implement policy access control, and set up reverse proxy junctions to process web requests. Hands-on exercises reinforce the skills learned.

*Note: this course is based upon IBM Security Verify Access v10.x.

IBM Security Guardium Data Protection Foundations

IBM Security® Guardium® Data Protection (Guardium) supports a zero trust approach to security. It discovers and classifies sensitive data from across an enterprise, providing real time data activity monitoring and advanced user behavior analytics to help discover unusual activity around sensitive data.

Guardium provides a broad range of data security and protection capabilities that can protect sensitive and regulated data across environments and platforms. This course provides the foundational level processes, procedures, and practices necessary to configure Guardium to monitor and protect sensitive data. Hands-on exercises reinforce the skills learned.

*Note: this course is based upon IBM Security® Guardium® Data Protection v11.4.

IBM QRadar SIEM Foundations

IBM Security QRadar enables deep visibility into network, endpoint, user, and application activity. It provides collection, normalization, correlation, and secure storage of events, flows, assets, and vulnerabilities. Suspected attacks and policy breaches are highlighted as offenses. In this course, you learn about the solution architecture, how to navigate the user interface, and how to investigate offenses. You search and analyze the information from which QRadar concluded a suspicious activity. Hands-on exercises reinforce the skills learned.

In this 3-day instructor-led course, you learn how to perform the following tasks:

• Describe how QRadar collects data to detect suspicious activities
• Describe the QRadar architecture and data flows
• Navigate the user interface
• Define log sources, protocols, and event details
• Discover how QRadar collects and analyzes network flow information
• Describe the QRadar Custom Rule Engine
• Utilize the Use Case Manager app
• Discover and manage asset information
• Learn about a variety of QRadar apps, content extensions, and the App Framework
• Analyze offenses by using the QRadar UI and the Analyst Workflow app
• Search, filter, group, and analyze security data
• Use AQL for advanced searches
• Use QRadar to create customized reports
• Explore aggregated data management
• Define sophisticated reporting using Pulse Dashboards
• Discover QRadar administrative tasks

Extensive lab exercises are provided to allow students an insight into the routine work of an IT Security Analyst operating the IBM QRadar SIEM platform. The exercises cover the following topics:

• Architecture exercises
• UI – Overview exercises
• Log Sources exercises
• Flows and QRadar Network Insights exercises
• Custom Rule Engine (CRE) exercises
• Use Case Manager app exercises
• Assets exercises
• App Framework exercises
• Working with Offenses exercises.
• Search, filtering, and AQL exercises
• Reporting and Dashboards exercises
• QRadar – Admin tasks exercises

The lab environment for this course uses the IBM QRadar SIEM 7.4 platform.

Red Hat Security: Linux in Physical, Virtual, and Cloud

The "Red Hat Security: Linux in Physical, Virtual, and Cloud" (RH415) course is designed for security and system administration personnel who manage the secure operation of computer systems running Red Hat Enterprise Linux on physical hardware, as virtual machines, or as cloud instances both in private data centers and on public cloud platforms.

Course description
• Maintaining the security of computing systems is a process of managing risk through the implementation of processes and standards backed by technologies and tools. "Red Hat Security: Linux in Physical, Virtual, and Cloud" (RH415) is designed for security administrators and system administrators who need to manage the secure operation of servers running Red Hat Enterprise Linux, whether deployed on physical hardware, as virtual machines, or as cloud instances. You will learn about technologies and tools that can be used to help you implement and comply with your security requirements, including the kernel's Audit subsystem, AIDE, SELinux, OpenSCAP and SCAP Workbench, USBGuard, PAM authentication, and Network-Based Device Encryption. You will learn to monitor compliance and to proactively identify, prioritize, and resolve issues by using OpenSCAP, Red Hat Insights, Red Hat Satellite, and Red Hat Ansible Automation Platform. You will have a basic introduction to how Red Hat Ansible Automation Platform automates the deployment of remediation to systems, by using Ansible Playbooks from OpenSCAP or Red Hat Insights.
• This course is based on RHEL 9.2, Ansible Core 2.14, Red Hat Ansible Automation Platform 2.4, Satellite 6.14, and OpenSCAP 1.3.7.
• Maintaining the security of computing systems is a process of managing risk through the implementation of processes and standards backed by technologies and tools. In this course, you will learn about resources that can be used to help you implement and comply with your security requirements.
• Following course completion, you will receive a 45-day extended access to hands-on labs for any course that includes a virtual environment.
• Note: This course is offered as a five day virtual class or self-paced. Durations may vary based on the delivery. For full course details, scheduling, and pricing, please fill out the form below.

Course summary
• Manage compliance with OpenSCAP.
• Enable SELinux on a server from a disabled state, perform basic analysis of the system policy, and mitigate risk with advanced SELinux techniques.
• Proactively identify and resolve issues with Red Hat Insights.
• Monitor activity and changes on a server with Linux Audit and AIDE.
• Protect data from compromise with USBGuard and storage encryption.
• Manage authentication controls with PAM.
• Manually apply provided Ansible Playbooks to automate mitigation of security and compliance issues.
• Scale OpenSCAP and Red Hat Insights management with Red Hat Satellite and Red Hat Ansible Automation Platform.

Target Audience
• System Administrator - responsible for supporting the company’s physical and virtual infrastructure, systems, and servers
• IT Security Practitioner / Compliance & Auditor - responsible for ensuring the technology environment is protected from attacks and is in compliance with security/privacy rules and regulations.
• Automation Architect - Engineer or architect responsible for the company’s automation development and optimizing cloud tools and infrastructure to achieve automation goals.

Cloud & Automation Instructor Lead Training

Enterprise Catalog Management and Data Protection with Watson Knowledge Catalog on IBM Cloud Pak for Data (V4.6)

Course Overview
This course provides Solution Architects an introduction to the basics of Watson Knowledge Catalog for IBM Cloud Pak for Data. You will learn to access the Watson Knowledge Catalog through the service, and gain skills in creating catalogs, populating them with assets, and then managing the assets in the catalog through a governance framework.

Course details
Audience

This course is designed for solution architects, but it is also relevant for other enterprise roles that want to understand and apply data governance, quality, workflow, and catalog concepts in Watson Knowledge Catalog.

Prerequisites

Before taking this course, you should be able to complete the following tasks:

• Explain the purpose of Cloud Pak for Data and the value it brings to the business
• Describe its basic architecture
• Differentiate between Cloud Pak for Data and Red Hat OpenShift Container Platform
• Define the AI Ladder and its associated roles and services
• Log in to Cloud Pak for Data and complete an analytics project
• Objectives

After completing this course, you should be able to:

• Summarize the foundational concepts of Watson Knowledge Catalog
• Define a governance workflow
• Create a governance framework that protects data
• Describe data by importing a business glossary
• Evaluate the contents of a data set
• Use governed data assets in projects

Administration of IBM Cloud Pak for Data (v4.5)

This course guides you through the most important administration activities that are related to the Cloud Pak for Data environment. You will recall the infrastructure of a Red Hat OpenShift cluster where Cloud Pak for Data runs, and you will learn how to manage this cluster. You will learn about multitenancy, tethered projects, the Cloud Pak for Data installation procedure, and prerequisites for various Cloud Pak for Data installation scenarios. A significant part of this course refers to tasks that an administrator must complete, including setting up an LDAP connection for user and group management, defining resource quotas and limits, and scaling services.

IBM Cloud Pak for AIOPs: Operating with Event Manager to reduce MTTR

This course teaches you how to use the Event Manager component of IBM Cloud Pak for AIOps to view, categorize, understand, and resolve alarm conditions in your IT event to reduce the mean time to resolve (MTTR) those issues.

Administering Environments with IBM Instana Observability

In this course, you learn how to perform observability and application performance monitoring functions using IBM Instana Observability. You learn the architecture of Instana and how the data flows from the sensors and tracers, through the agents, and to the big data backend. You learn essential user interface techniques and how to create application perspectives and to use their dashboards to monitor the services. In addition, this course describes distributed tracing and how IBM Instana AutoTrace works and how to quickly trace issues down to the root cause. You also learn how the differences between changes, issues, and incidents and how to create alerts to notify team members when certain criteria is met. You experiment with establishing roles for users with group permissions. And you learn how to create custom dashboards to help you keep track of the things for which you are responsible.

Configuring IBM Cloud Pak for Watson AIOps Event Manager

The Event Manager component of IBM Cloud Pak for Watson AIOps is a carrier-class service assurance system. It collects and consolidates events and alarms from a wide variety of IT environments in real time. These include servers, mainframes, Windows systems, applications, circuit switches, voice switches, IP routers, SNMP devices, network management applications, existing management systems and frameworks, among many others.

IBM Cloud Pak for Watson AIOps Event Manager also adds intelligence to your events, allowing you to cast a wide net to ingest relevant data from any source, process it in an intelligent, automated way, analyze the data, see which applications or parts of the infrastructure are impacted, share it and even suggest guided steps to mitigate or resolve issues automatically.

One key benefit of Event Manager's machine learning features is a reduction in the number of events. By detecting, correlating, grouping, and suppressing the "noise" that IT systems generate, your operators can focus their attention on key events that represent actual problems.

This 2-day course teaches you how to configure IBM Cloud Pak for Watson AIOps Event Manager for productive use. Through hands-on lab activities, you learn how to configure a new installation of Event Manager to:

• Connect to event sources and enrich incoming events
• Apply machine learning to find relationships among events
• Add topology data to identify groups of connected resources and calculate root cause
• Create automated fixes for known problems and match them to incoming problem events
You also get hands-on practice with other common configuration tasks, such as user management and database customization. This course focuses on IBM Cloud Pak for Watson AIOps Event Manager running on the Red Hat OpenShift Container Platform.

IBM Cloud Pak for Watson AIOps Administration

IBM Cloud Pak for Watson AIOps deploys advanced, explainable AI across the IT Operations (ITOps) toolchain so that you can confidently assess, diagnose, and resolve incidents across mission-critical workloads.

IBM Cloud Pak for Watson AIOps brings the depth and breadth of IBM’s enterprise expertise to managing complex, mission-critical IT environments. IBM Cloud Pak for Watson AIOps helps you apply AI to IT operations to maximize efficiency, reduce costs, and maintain the resiliency and security you need to drive meaningful innovation.

This course is designed to teach you how to perform important administration tasks for the IBM Cloud Pak for Watson AIOps platform, such as user management, troubleshooting, and self-monitoring.

Red Hat System Administration I - Red Hat® Enterprise Linux 9.0

The first of two courses covering the core system administration tasks needed to manage Red Hat Enterprise Linux servers

Red Hat System Administration I (RH124) is designed for IT professionals without previous Linux system administration experience. The course provides students with Linux administration competence by focusing on core administration tasks. This course also provides a foundation for students who plan to become full-time Linux system administrators by introducing key command-line concepts and enterprise-level tools.

This course is the first of a two-course series that takes a computer professional without Linux system administration knowledge to become a fully capable Linux administrator. These concepts are further developed in the follow-on course, Red Hat System Administration II (RH134).

This course is based on Red Hat® Enterprise Linux 9.0.

Following course completion, you will receive a 45-day extended access to hands-on labs for any course that includes a virtual environment.

Note: This course is offered as a five day in person class, a five day virtual class or is self-paced. Durations may vary based on the delivery. For full course details, scheduling, and pricing, please fill out the form below.

Course content summary
• Introduce Linux and the Red Hat Enterprise Linux ecosystem.
• Run commands and view shell environments.
• Manage, organize, and secure files.
• Manage users, groups and user security policies.
• Control and monitor systems services.
• Configure remote access using the web console and SSH.
• Configure network interfaces and settings.
• Manage software using DNF

Audience for this course
• The primary persona is a technical professional with current or pending responsibilities as a Linux enterprise or cloud system administrator.
• This course is popular in Red Hat Academy, and is targeted at the student who is planning to become a technical professional using Linux.

RedHat OpenShift Administration I: Operating a Production Cluster

Deploy, manage, and troubleshoot containerized applications running as Kubernetes workloads in OpenShift clusters.

Course Description
Red Hat OpenShift Administration I: Managing Containers and Kubernetes (DO180) prepares OpenShift cluster administrators to manage Kubernetes workloads and to collaborate with developers, DevOps engineers, system administrators, and SREs to ensure the availability of application workloads. This course focuses on managing typical end-user applications that are often accessible from a web or mobile UI and that represent most cloud-native and containerized workloads. Managing applications also includes deploying and updating their dependencies, such as databases, messaging, and authentication systems.

The skills that you learn in this course apply to all versions of OpenShift, including Red Hat OpenShift on AWS (ROSA), Azure Red Hat OpenShift, and OpenShift Container Platform.

This course is based on Red Hat OpenShift 4.14.

Following course completion, you will receive a 45-day extended access to hands-on labs for any course that includes a virtual environment.
Note: This course is offered as a four day in classroom, a five day virtual class, or self-paced. Durations may vary based on the delivery. For full course details, scheduling, and pricing, please fill out the form below.

Course Content Summary
• Managing OpenShift clusters from the command-line interface and from the web console
• Deploying applications on OpenShift from container images, templates, and Kubernetes manifests
• Troubleshooting network connectivity between applications inside and outside an OpenShift cluster
• Connecting Kubernetes workloads to storage for application data
• Configuring Kubernetes workloads for high availability and reliability
• Managing updates to container images, settings, and Kubernetes manifests of an application

Target Audience
• Primary: Platform Engineers, System Administrators, Cloud Administrators, and other infrastructure-related IT roles who are responsible for tier-1 support of infrastructure for applications.who are interested in managing OpenShift clusters and containerized applications.
• Secondary: Enterprise Architects, Site Reliability Engineers, DevOps Engineers, and other application-related IT roles who are responsible for designing infrastructure for applications.
• Developers and Site Reliability Engineers that are new to container technology should enroll in Red Hat OpenShift Development I: Introduction to Containers with Podman (DO188).

Red Hat Enterprise Linux Automation with Ansible

Course description
Learn how to automate Linux system administration tasks with Red Hat Ansible Automation Platform

Red Hat Enterprise Linux Automation with Ansible (RH294) is designed for Linux administrators and developers who need to automate repeatable and error-prone steps for system provisioning, configuration, application deployment, and orchestration.

This course is based on Red Hat® Enterprise Linux® 9 and Red Hat Ansible Automation Platform 2.2.

Following course completion, you will receive a 45-day extended access to hands-on labs for any course that includes a virtual environment.

Note: This course is offered as a four day in person class, a five day virtual class or is self-paced. Durations may vary based on the delivery. For full course details, scheduling, and pricing, please fill out the form below.

Course content summary
• Install Red Hat Ansible Automation Platform on control nodes.
• Create and update inventories of managed hosts and manage connections to them.
• Automate administration tasks with Ansible Playbooks and ad hoc commands.
• Write effective playbooks at scale.
• Protect sensitive data used by Ansible Automation Platform with Ansible Vault.
• Reuse code and simplify playbook development with Ansible Roles and Ansible Content Collections.

Audience for this course
This course is geared toward Linux system administrators, DevOps engineers, infrastructure automation engineers, and systems design engineers who are responsible for these tasks:
• Automating configuration management
• Ensuring consistent and repeatable application deployment
• Provisioning and deployment of development, testing, and production servers
• Integrating with DevOps continuous integration/continuous delivery workflows

Developing Cloud-Native Applications with Microservices Architectures

Course description
Identify the proper frameworks and tools to build your microservices architecture

Developing Cloud-Native Applications with Microservices Architectures (DO092) is a series of on-demand, online videos that will teach you how to combine different frameworks and tools into a microservices architecture that fits your organizational needs.

You’ve no doubt heard about the microservices architecture, but understanding and executing it can be a bit of a challenge. Through a series of videos, this course will introduce microservices, review multiple microservices frameworks and runtimes, and show you techniques to deploy them through a hassle-free DevOps pipeline. We’ll discuss containers, Docker, Spring Boot, NodeJS, .NET, OpenShift, Jenkins, Vert.x, Kubernetes, and much more.

Note: You can view all videos or only the ones that interest you. There are no hands-on labs or course completion recognition associated with this course.

Audience for this course
Java™ developers and anyone interested in OpenShift and Kubernetes

Understanding of software and IT system architecture

Introduction to Red Hat OpenShift Service on AWS (ROSA)

Course description
Learn how to deploy, access, and perform basic customizations to a ROSA cluster.

This course teaches Platform Operators how to provision managed clusters by using Red Hat OpenShift Service on AWS (ROSA) and how to perform basic day-2 customizations on these clusters to onboard application developers and applications.

Following course completion, you will receive a 45-day extended access to hands-on labs for any course that includes a virtual environment.

Note: This course is self paced. Durations may vary based on the delivery. For full course details, scheduling, and pricing, please fill out the form below.

Course content summary
• Introduction to Managed OpenShift
• Identify prerequisites to create and deploy a ROSA cluster
• Access a ROSA cluster as an administrator and as a developer self-service user
• Connect a ROSA cluster to Red Hat Services
• Configure additional storage classes and configure log forwarding to AWS CloudWatch
• Create dedicated node pools and configure node autoscaling

Target Audience
• System administrators, platform operators, and cloud engineers.
• Enterprise and cloud-native application developers.
• Application and development infrastructure professionals such as site reliability engineers and DevOps engineers.

Recommended training
• All students must be knowledgeable of Amazon Web Services (AWS), including operating and managing AWS compute, storage, and network resources.
• For students that are new to Red Hat OpenShift it is recommended that you learn the fundamental skills of managing Red Hat OpenShift clusters, before taking DO120, from the following courses:
o Red Hat OpenShift I: Containers & Kubernetes (DO180)
o Red Hat OpenShift Administration II: Operating a Production Kubernetes Cluster (DO280)
• Students with previous experience managing Kubernetes clusters are advised to take DO180 and DO280 before taking DO120 or at least acquire foundational skills operating Red Hat OpenShift clusters by using the following free resources from Red Hat:
o Red Hat Developer Sandbox for OpenShift
o OpenShift and Kubernetes learning from Red Hat Developer
o Containers, Kubernetes and Red Hat OpenShift Technical Overview (DO080)

Technology considerations
• Internet access is required to access AWS cloud services using the AWS console and the AWS CLI. It is also required to access the Red Hat Hybrid Cloud Console and associated Red Hat cloud services.
• Students must possess an active AWS account with permission to activate services from the AWS Marketplace and an associated payment method for the AWS resources consumed by ROSA clusters.
• Students must possess an active Red Hat customer portal account or a free Red Hat Developer program membership.

Introduction to Microsoft Azure Red Hat OpenShift

Course description
Learn how to deploy, access, and perform basic customizations to an Microsoft Azure Red Hat OpenShift cluster.

DO121 teaches Platform Operators how to provision managed clusters by using Microsoft Azure Red Hat OpenShift and how to perform basic day-2 customizations on these clusters to onboard application developers and applications.

Following course completion, you will receive a 45-day extended access to hands-on labs for any course that includes a virtual environment.

Note: This course is self paced. Durations may vary based on the delivery. For full course details, scheduling, and pricing, please fill out the form below.

Course content summary
• Introduction to Managed OpenShift
• Identify prerequisites to create a Microsoft Azure Red Hat OpenShift cluster
• Access a Microsoft Azure Red Hat OpenShift cluster as an administrator and as a developer self-service user
• Connect a Microsoft Azure Red Hat OpenShift cluster to Red Hat Services
• Configure additional storage classes and configure log forwarding to Azure Monitor
• Create dedicated node pools and configure node autoscaling

Audience for this course
• IT Operations professionals such as System Administrators, Platform Operators, Cloud Engineers.
• Application and development infrastructure professionals such as Site Reliability Engineers and DevOps Engineers.

Recommended training
• All students must be knowledgeable of Microsoft Azure, including operating and managing Azure compute, storage, and network resources.
• For students that are new to Red Hat OpenShift it is recommended that you learn the fundamental skills of managing Red Hat OpenShift clusters, before taking DO121, from the following courses:
o Red Hat OpenShift I: Containers & Kubernetes (DO180)
o Red Hat OpenShift Administration II: Operating a Production Kubernetes Cluster (DO280)
• Students with previous experience managing Kubernetes clusters are advised to take DO180 and DO280 before taking DO121 or at least acquire foundational skills operating Red Hat OpenShift clusters by using the following free resources from Red Hat:
o Red Hat Developer Sandbox for OpenShift
o OpenShift and Kubernetes learning from Red Hat Developer
o Containers, Kubernetes and Red Hat OpenShift Technical Overview (DO080)

Technology considerations
• Internet access is required to access Azure cloud services using the Azure Portal and the Azure CLI. It is also required to access the Red Hat Hybrid Cloud Console and associated Red Hat cloud services.
• Students must possess an active Azure account with an associated payment method for the Azure resources consumed by Microsoft Azure Red Hat OpenShift clusters.
• Students must possess an active Red Hat customer portal account or a free Red Hat Developer program membership.

Red Hat DevOps Pipelines and Processes: CI/CD with Jenkins, Git, and Test Driven Development

Course description
Build essential skills to implement agile and DevOps development processes and workflows.

DevOps practices have enabled organizations to undergo a digital transformation, moving from a monolithic waterfall approach to a rapidly deploying cloud-based agile process. This transformation requires a team of developers trained to use tools that enable them to spend more time coding and testing and less time troubleshooting. Red Hat DevOps Pipelines and Processes: CI/CD with Jenkins, Git, and Test-Driven Development (TDD) is a practical introduction to DevOps for developers that teaches students the necessary skills and technologies for automated building and deploying of cloud-native applications.

Following course completion, you will receive a 45-day extended access to hands-on labs for any course that includes a virtual environment.

Note: This course is self paced. Durations may vary based on the delivery. For full course details, scheduling, and pricing, please fill out the form below.

Course content summary
• Version control with Git
• Build and execute Jenkins pipelines
• Release strategies
• Build applications with Test Driven Development
• Security scanning and code analysis of applications
• Monitor applications and pipelines
• Consume and troubleshoot pipelines

Audience for this course
This course is designed for application developers.

Recommended training
Experience with application development in Java, Node.js, Python, or others is required.
Experience with application development or Red Hat Application Development I: Programming in Java EE (AD183) is recommended, but not required.
Proficiency in using an IDE such as Red Hat® Developer Studio or VSCode
Introduction to OpenShift Applications (DO101) is recommended, but not required.
Take our free assessment to gauge whether this offering is the best fit for your skills.

Technology considerations
Internet access required.

You will use your own machines and must be able to install software on your device. If you are unable to do so, you may use the embedded virtual machine in ROL or be provided a machine in ILT.

Microsoft Windows Automation with Red Hat Ansible

Course description
Learn how to automate administration on Windows Server to enable your DevOps workflow

Microsoft Windows Automation with Red Hat Ansible (DO417) is designed for Windows Server professionals without previous Ansible® experience. You will use Ansible to write automation playbooks for Microsoft Windows systems to perform common system administration tasks reproducibly at scale. You will also learn to use Red Hat® Ansible Tower to securely manage and run your Ansible playbooks from a central web-based user interface.

This course is based on Red Hat Ansible Engine 2.8, Red Hat Ansible Tower 3.5, and Windows Server 2016 and 2019.

Following course completion, you will receive a 45-day extended access to hands-on labs for any course that includes a virtual environment.

Note: This course is five days. Durations may vary based on the delivery. For full course details, scheduling, and pricing, please fill out the form below.

Course content summary
• Configure Microsoft Windows systems to be managed with Ansible.
• Create and manage inventories of managed hosts and provide credentials to manage them to Red Hat Ansible Tower.
• Write Ansible playbooks to consistently automate multiple tasks and apply them to managed hosts.
• Run individual ad hoc automation tasks and complex playbooks from Red Hat Ansible Tower.
• Create survey forms in Red Hat Ansible Tower to simplify playbook operation.
• Parameterize playbooks using variables and facts.
• Write and reuse existing Ansible roles to simplify playbook creation and reuse code.
• Leverage existing PowerShell DSC code to extend the power of Ansible automation.
• Automate common Windows Server system administration tasks using Ansible.

Audience for this course
Windows Server administrators interested in automating management tasks and in using automation tools to implement their DevOps workflow.

Prerequisites for this course
You are expected to have experience as Windows Server administrators, but no previous experience with Red Hat Ansible Automation or Linux® is required.

Technical requirements
• This class will require internet access.
• You will be expected to “bring your own device” (BYOD).
• Your device must be installed with a Remote Desktop Protocol (RDP).
o If you are running Microsoft Windows, you should have Microsoft Remote Desktop installed.
o If you are running macOS, you will need to install Microsoft Remote Desktop for Mac (by Microsoft) from the App Store.
o If you are running Linux, you may install Remmina from their distribution (if available) or following instructions at https://remmina.org (if not). If they prefer, they may instead install the FreeRDP clients from their Linux distribution (in the freerdp package in Red Hat Enterprise Linux).

Network Automation with Red Hat Ansible Automation Platform

Configure and manage network infrastructure using Red Hat Ansible Automation Platform.

Course Description
Network Automation with Red Hat Ansible Automation Platform (DO457) is designed for network administrators or infrastructure automation engineers who want to use network automation to centrally manage the switches, routers, and other devices in the organization's network infrastructure. Learn how to use Red Hat Ansible Automation Platform to remotely automate the configuration of network devices, test and validate the current network state, and perform compliance checks to detect and correct configuration drift.

This course is based on Red Hat® Ansible Automation Platform 2.3

Following course completion, you will receive a 45-day extended access to hands-on labs for any course that includes a virtual environment.

Note: This course is offered as a five day virtual class or self-paced. Durations may vary based on the delivery. For full course details, scheduling, and pricing, please fill out the form below.

Course summary
• Prepare a development environment for Ansible network automation
• Write and troubleshoot effective Ansible Playbooks for network automation
• Gather information about network infrastructure configuration for infrastructure awareness and configuration backup
• Automate specific network administration use cases, including configuration of routers and switches, ports, VLANs, SNMP monitoring, and routing protocols
• Use Ansible Playbooks to manage devices from various hardware vendors, including Cisco, Juniper, and Arista
• Centrally manage Ansible content in Git and run it centrally with automation controller
• Reuse existing, tested network automation code with Ansible Roles, Ansible Content Collections, and Ansible validated content

Target Audience
This course is designed for network administrators, network automation engineers, and infrastructure automation engineers who are responsible for deploying, managing, and automating the network infrastructure of their organization or enterprise.

Recommended Training
• Take our free assessment to gauge whether this offering is the best fit for your skills
• Experience with network administration, including a solid understanding of TCP/IP, routers, and managed switches
• Familiarity with managing network devices from the command line, preferably with one or more of Cisco IOS, IOS XR, or NX-OS; Juniper Junos; or Arista EOS
• Knowledge equivalent to Red Hat System Administration I (RH124) or better is recommended
• Prior Ansible knowledge is not required

Technology Considerations
• For virtual or self-paced learners:
o Internet access is required to run the exercises and labs
o BYOD/BYDW is not supported
• For classroom learners:
o Internet access is not required
o BYOD/BYDW is not supported

Red Hat Ansible Automation Platform Boot Camp

Course Description
Learn how to automate Linux system administration tasks with Red Hat Ansible Automation Platform and manage complex automation workflows at scale and prevent single points of failure.

• The Ansible Automation Platform Boot Camp (DO710) is designed for Linux administrators and developers who need to automate repeatable and error-prone steps for system provisioning, configuration, application deployment, and orchestration. Learn recommended practices for automation development using reusable code, advanced playbook techniques, shared execution environments, and preparing for scalable automation with the automation content navigator. Deploy automation controller to centrally manage automation workflows, automation mesh to scale up and distribute execution capacity, and private automation hub to manage Ansible Content Collections and automation execution environments for use by automation developers.
• This collection of courses is based on Red Hat Ansible Automation Platform 2.2.
• As part of enrollment, you will receive one year of Red Hat Learning Subscription Standard, which gives you unlimited access to all of our courses online, plus up to five certification exams.

Following course completion, you will receive a 45-day extended access to hands-on labs for any course that includes a virtual environment.

Note: This course is offered as a ten day virtual class. Durations may vary based on the delivery. For full course details, scheduling, and pricing, please fill out the form below.

Course Content Summary
• Installing Red Hat Ansible Automation Platform on control nodes.
• Creating and updating inventories of managed hosts and managing connections to them.
• Automating administration tasks with Ansible Playbooks.
• Writing effective playbooks at scale.
• Protecting sensitive data used by Ansible Automation Platform with Ansible Vault.
• Reusing code and simplifying playbook development with Ansible Roles and Ansible Content Collections.
• Apply recommended practices for effective and efficient automation with Ansible.
• Perform automation operations as rolling updates.
• Use advanced features of Red Hat Ansible Automation Platform to work with data, including filters and plugins.
• Create automation execution environments to contain and scale Red Hat Ansible Automation.
• Leverage capabilities of the automation content navigator to develop Ansible Playbooks.
• Discussion of the architecture of Red Hat Ansible Automation Platform 2.
• Installation and configuration of automation controllers and private automation hubs to centrally coordinate and scale Red Hat Ansible Automation.
• Integration of Red Hat Ansible Automation Platform with centralized Git repository services such as GitLab.
• Management of users, teams, and access permissions in Red Hat Ansible Automation Platform services.
• Creation and management of workflows that execute automation based on the success or failure of previous jobs
• Troubleshooting and maintenance of Red Hat Ansible Automation Platform services.
• Discussion of recommended practices to ensure high availability and scalability of a large automation cluster.

Target Audience
This course is geared toward Linux system administrators, DevOps engineers, Site Reliability Engineers, infrastructure automation engineers, and developers who are responsible for repeatable tasks such as:
• Automating configuration management
• Ensuring consistent and repeatable application deployment
• Provisioning and deployment of development, testing, and production servers
• Integrating with DevOps continuous integration/continuous delivery workflows

Red Hat Ansible Automation for SAP

Course description
Learn how to automate the building of SAP environments using Red Hat Ansible.

Red Hat Ansible Automation for SAP Technical Overview (RH045) is a series of free on-demand, online videos that explain how to automate the deployment, configuration, creation, and migration of SAP workloads using Red Hat Ansible. Focusing on day-1 operations, Red Hat Senior Architect Ricardo García Cavero demonstrates how to install and deploy SAP HANA and SAP S/4HANA applications using Red Hat Ansible Tower, as well as how to create, migrate, and configure environments forSAP workloads.

Audience for this course
IT leaders, SAP administrators, engineers, solution architects, and anyone else seeking a high-level understanding of using Red Hat Ansible to automate SAP workloads.

Recommended training
Basic system administration skills with a fundamental understanding of SAP and automation concepts.