What if I miss a DevOps online training class?

You will never miss a lecture at Edureka! You can choose either of the two options: View the recorded session of the class available in your LMS OR You can attend the missed session, in any other live batch. So enroll now with one of the best DevOps Certification courses online and never miss a single class.

What if I have queries after I complete this DevOps online course?

Your access to the Support Team is for lifetime and will be available 24/7. The team will help you in resolving queries, during and after the DevOps course in Seattle.

How soon after Signing up would I get access to the Learning Content?

Post-enrolment of DevOps certification course in Seattle, the LMS access will be instantly provided to you and will be available for a lifetime. You will be able to access the complete set of previous class recordings, PPTs, PDFs, assignments. Moreover, access to our 24x7 support team will be granted instantly as well. You can start learning right away.

Is the course material accessible to the students even after the DevOps online training is over?

Yes, the access to the DevOps certification course material will be available for lifetime once you have enrolled into the course. So enroll now with our best DevOps course in Seattle and enjoy the benefit of lifetime access to the learning material.

Why should I learn DevOps course online? How is it better than offline training?

Learning pedagogy has evolved with the advent of technology. Our DevOps online training adds convenience and quality to the training module. With our 24x7 support system, our online learners will have someone to help them all the time even after the class ends. This is one of the driving factors to make sure that people achieve their end learning objective. We also provide life-time access of our updated course material to all our learners. So join our best DevOps Certification Course online now.

How long will it take to complete the DevOps course in Seattle?

Edureka’s DevOps certification training offers variable batch schedule to suit everyone’s needs. The weekend batches run for 6 weeks of live instructor led sessions. Which is then followed by real-time project for better hands-on. The accelerated program or the weekday batches can be completed in much shorter time with rigorous training sessions and live project to work-on at the end. Edureka offers the best DevOps course online so that you can learn with the top India's instructors.

What skills should a DevOps engineer have?

Familiarity with Linux Fundamentals and knowledge of one Scripting language, such as Python or Shell would help.A DevOps Engineer should be proficient with various Development, Testing, and Deployment technologies. He/She should understand various DevOps stages and should have experience with various DevOps tools, including Git, Jenkins, Docker, Selenium, Puppet, Chef, Kubernetes and Nagios.A DevOps engineer should have experience in developing and maintaining CI/CD pipelines. Good hands-on knowledge of a cloud service (AWS, Azure etc.) can be an added advantage.

What are the roles and responsibilities of DevOps engineer?

One of the main roles of DevOps engineer is to be an excellent communicator. The major focus for DevOps engineer is to bridge the gap between the development and the IT operations team. They serve as an integral member of both the teams and liaises between both. While the demands of a DevOps Engineer often require him or her to work alone, they are expert collaborators.

What is a DevOps lifecycle?

DevOps is a software development process which involves Continuous Development, Continuous Testing, Continuous Integration, Continuous Deployment and Continuous Monitoring of the software throughout its development life cycle. These activities are possible only in DevOps, not Agile or waterfall, and this is why Facebook and other top companies have chosen DevOps as the way forward for their business goals.

What is the average salary of a DevOps certified professional in Seattle?

As per Payscale, the average salary of a DevOps professional is $96,418 in the Seattle, United States.

What is the DevOps Course fee in Seattle?

The cost of DevOps Training in Seatlle is $449.

DevOps Training in Seattle [#1 Institute for DevOps Course]

Name: DevOps Certification Training Course with Gen AI
Item: DevOps Certification Training Course with Gen AI
Rating: 5
Author: Vijay Kalkundri

Why should you take DevOps Certification Training in Seattle?

Average salary of DevOps Engineer in Seattle is $120,000.

Companies hiring devops professionals in Seattle – Rackspace, Dell, Oracle, HP & many more

According to Linkedin, 327+ DevOps jobs available in Seattle

DevOps Fundamentals and CI/CD Overview

Topics Covered:

Evolution of software development methodologies
DevOps culture and organizational impact
Core DevOps principles: collaboration, automation, measurement, sharing
Key DevOps practices and lifecycle
DevOps reference architectures and implementation strategies
Overview of CI/CD pipelines
Value stream mapping and process optimization
Introduction to DevOps toolchain
AI-driven automation concepts in DevOps (e.g., predictive analytics for bottleneck identification)
Industry case studies of successful DevOps transformations

Hands-on Activities:

Assessment of current workflow bottlenecks and improvement opportunities
Value Stream Mapping exercise to identify waste in the software delivery pipeline
Setting up a basic CI pipeline structure diagram
Tool exploration and environment setup for subsequent modules

Skills Covered:

Understanding DevOps culture and CI/CD principles
Mapping and optimizing delivery workflows
Applying AI for workflow automation and predictive analytics

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Version Control with Git and GitHub

Topics Covered:

Git architecture and core concepts
Repository management and Git commands
Branching strategies: GitFlow, GitHub Flow, Trunk-based development
Pull requests and code review processes
Merge conflict resolution techniques
Git hooks for automation
GitHub Actions introduction
AI-powered code review with GitHub Copilot
Repository security best practices

Hands-on Activities:

Create and manage branches, resolve conflicts
Configure protected branches and access controls
Build a simple GitHub Actions workflow
Set up GitHub repositories with automated code review workflows using AI tools
Resolve merge conflicts with AI-assisted suggestions

Skills Covered:

Mastering Git workflows and resolving merge conflicts
Collaborating via GitHub and managing repository security
Using AI tools for automated code reviews and suggestions

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Continuous Integration with Jenkins

Topics Covered:

Jenkins architecture and components
Jenkins installation and configuration
Pipeline as Code with Jenkinsfile
Jenkins plugins ecosystem
Parameterized builds and triggers
Automated testing and static code analysis
Pipeline optimization techniques
Jenkins security and credentials management
Multi-branch and pipeline visualization
AI-driven test selection and deployment risk analysis

Hands-on Activities:

Installing and configuring the Jenkins server
Creating declarative and scripted pipelines
Configuring build triggers and webhooks with Git repositories
Setting up automated testing in the CI pipeline
Implementing pipeline visualization
Secure Jenkins with role-based access control
Debugging pipeline failures
Configure Jenkins pipelines with AI-based test case prioritization tools (e.g., Testim.io)
Implement predictive analytics for deployment success rates

Skills Covered:

Designing CI pipelines with Jenkins and Jenkinsfile
Automating testing and integrating static code analysis
Leveraging AI for test case prioritization and pipeline optimization

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Infrastructure as Code (IaC) with Terraform and CloudFormation

Topics Covered:

IaC concepts and benefits
Terraform basics: HCL, providers, state management
AWS CloudFormation fundamentals
State management in Terraform
Modules, variables, and outputs
Security in IaC: secrets management and policy enforcement
Testing infrastructure code
AI-assisted code generation using HashiCorp Waypoint AI and Tabnine
Anomaly detection in infrastructure metrics
Resource cost prediction and optimization using ML models (e.g., AWS Forecast)

Hands-on Activities:

Write Terraform scripts to provision AWS resources
Create CloudFormation templates for infrastructure
Manage Terraform state and remote backends
Integrating Terraform with CI/CD pipeline
Security scanning for infrastructure code
Infrastructure testing with Terratest
Write Terraform scripts with AI-generated suggestions for optimization
Use AI models to predict resource allocation needs in cloud environments

Skills Covered:

Writing and managing reusable Terraform and CloudFormation templates
Integrating IaC into CI/CD workflows
Using AI to auto-generate, validate, and optimize IaC scripts

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Configuration Management with Ansible

Topics Covered:

Configuration management concepts and tools
Ansible architecture and components
YAML syntax for Ansible
Playbooks, roles, and inventories
Variables, templates, and conditionals
Ansible Vault for secrets
Error handling and idempotency
Ansible integration with CI/CD tools
AI-assisted playbook generation using tools like GitHub Copilot, Tabnine, or CodeWhisperer
Natural Language to YAML: Prompt-to-playbook using LLMs (e.g., OpenAI, Codex)
Self-healing scripts with AI triggers and remediation logic

Hands-on Activities:

Writing and executing basic Ansible playbooks
Developing reusable Ansible roles
Create roles for modular automation
Implementing secret management with Ansible Vault
Creating dynamic inventories
Trigger Ansible playbooks from Jenkins
Use GitHub Copilot or CodeWhisperer for assisted playbook authoring

Skills Covered:

Creating and organizing Ansible playbooks and roles
Managing secrets securely with Ansible Vault
Using AI for playbook generation and self-healing logic implementation

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Containerization with Docker

Topics Covered:

Container concepts and Docker architecture
Dockerfile syntax and best practices
Image layering and optimization
Docker networking models
Volume management and data persistence
Docker Compose for multi-container apps
Container registries and image scanning
Runtime security and resource limits
AI-based image scanning (e.g., Trivy) for security vulnerabilities
AI-based Dockerfile generation using tools like GitHub Copilot, Tabnine, or Codex

Hands-on Activities:

Create Dockerfiles and build images
Building, tagging, and pushing images to registries
Deploy multi-container apps with Docker Compose
Setting up a private container registry with security controls
Integrating Docker builds in the CI pipeline
Scan images for vulnerabilities using Trivy
Create Dockerfiles with AI-generated optimizations for performance improvements.

Skills Covered:

Building and optimizing Docker images and Dockerfiles
Managing container lifecycle with Compose and registries
Using AI for vulnerability scanning and Dockerfile optimization

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Container Orchestration with Kubernetes

Topics Covered:

Kubernetes architecture and core components
Pods, ReplicaSets, Deployments, and DaemonSets
Kubernetes Services: ClusterIP, NodePort, LoadBalancer
ConfigMaps, Secrets, and Environment Configuration
Namespaces and RBAC for multi-tenancy and security
Kubernetes CLI (kubectl) for cluster management
Pod lifecycle management and scaling strategies
Rolling updates, rollbacks, and deployment strategies
AI-assisted Deployment Strategies
Using AI to Analyze Pod Health and Recommend Restarts or Scaling

Hands-on Activities:

Set up a Minikube or Kind cluster
Deploy sample applications using YAML manifests
Use kubectl for managing Pods, Services, and Deployments
Implement rolling updates and scale applications
Manage secrets and config using ConfigMaps and Secrets
Apply RBAC policies to restrict access by role and namespace
Integrate a simple AI script to monitor pod resource usage and recommend scaling
Simulate and deploy a predictive health-check model for early pod failure detection

Skills Covered:

Managing deployments, updates, and scaling in Kubernetes
Securing workloads with RBAC and secrets
Integrating AI to monitor pod health and recommend scaling actions

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Kubernetes Networking, Storage, and Advanced Tooling

Topics Covered:

Labels, Selectors, Annotations, and Service Discovery
Types of Services: ClusterIP, NodePort, LoadBalancer, Headless
Persistent Volumes (PVs), Persistent Volume Claims (PVCs), Storage Classes
Ephemeral vs. Projected Volumes and Access Modes
StatefulSets for stateful workloads
Helm: Kubernetes package manager and Helm Charts
Prometheus setup and metrics scraping for Pods, Nodes, and Deployments
Grafana dashboards for monitoring Kubernetes environments
Alerting with Grafana and using Prometheus exporters
Security best practices and Pod Security Policies

Hands-on Activities:

Deploy Services and explore networking with demonstrations
Create and mount Persistent Volumes and use PVCs
Deploy stateful applications with StatefulSets
Use Helm to deploy pre-packaged applications
Install Prometheus and Grafana for monitoring
Create dashboards and set alerts based on Kubernetes metrics

Skills Covered:

Configuring persistent storage and Kubernetes Services
Deploying applications using Helm charts
Visualizing cluster performance with Prometheus and Grafana

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Monitoring and Logging with Prometheus, Grafana, and ELK Stack

Topics Covered:

Monitoring principles and the four golden signals
Prometheus architecture and data model
Prometheus setup and PromQL queries
Grafana dashboard creation
ELK Stack components (Elasticsearch, Logstash, Kibana)
Log aggregation strategies
Alerting and notification configuration
SLIs, SLOs, and SLAs
Performance analysis techniques
Distributed tracing concepts

Hands-on Activities:

Setting up Prometheus and Grafana for application monitoring
Implementing custom metrics in applications
Designing comprehensive Grafana dashboards
Configuring the ELK Stack for log aggregation
Creating logging pipelines with Logstash
Implementing alerting rules and notification channels
Log querying and analysis in Kibana
Monitoring infrastructure and application health
Use OpenAI to classify log messages and flag potential errors

Skills Covered:

Setting up monitoring and log aggregation pipelines
Creating actionable Grafana dashboards and alerts
Applying AI for anomaly detection in metrics and logs

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DevSecOps: Security Integration in DevOps Pipelines

Topics Covered:

Security as Code concept
DevSecOps principles and implementation approaches
Static Application Security Testing (SAST)
Dynamic Application Security Testing (DAST)
Software composition analysis (SCA)
Container and infrastructure security scanning
Secrets management (HashiCorp Vault, AWS Secrets Manager)
Security policy as code (OPA, Kyverno)
Compliance as Code
Security monitoring and intrusion detection
Incident response and audit trails

Hands-on Activities:

Integrating SAST tools (SonarQube) in the CI pipeline
Implementing DAST with OWASP ZAP in the deployment pipeline
Setting up secrets management with HashiCorp Vault
Perform dependency vulnerability scanning with Snyk or similar
Implementing security policies with Open Policy Agent
Automated compliance checking with InSpec

Skills Covered:

Automating SAST, DAST, and compliance checks
Securing credentials and implementing policy-as-code
Performing vulnerability scanning and incident response

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Generative AI Integration in DevOps

Topics Covered:

Gen AI concepts: LLMs, transformers, and diffusion models
AI coding assistants (GitHub Copilot, Amazon CodeWhisperer)
Prompt engineering for infrastructure and automation scripts
AI for automated documentation and test generation
Ethical considerations and AI governance in DevOps

Hands-on Activities:

Generate Terraform and Ansible scripts using ChatGPT
Create AI-assisted Dockerfiles and Kubernetes manifests
Automate test case generation with AI tools
Implement AI-powered chatops in Slack or MS Teams

Skills Covered:

Generating infrastructure code using AI assistants
Automating documentation and test generation with GenAI
Integrating AI chatops and prompt-based automation

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End-to-End AI-Enhanced CI/CD Pipeline

Project Scope:

Use Git/GitHub for version control and collaboration
Build a Jenkins pipeline integrating Terraform, Ansible, Docker, and Kubernetes
Incorporate AI tools for code generation, testing, and monitoring
Implement security gates and automated compliance checks
Deploy a containerized application with monitoring and alerting

Hands-on Activities:

Designing a comprehensive CI/CD pipeline architecture
Implementing complete pipelines with Jenkins or GitHub Actions
Integrating all tooling components: VCS, testing, security scanning, IaC, configuration management
Building a containerized application deployment with Kubernetes
Implementing monitoring and alerting
Creating pipeline documentation and runbooks

Skills Covered: