Site Reliability Engineering (SRE) Practitioner™

 

•  7 modules
•  3 and more learning resources
• PDF Manuals 
• Recorded Video

 

- Designs and implements systems that improve the reliability and performance of websites and applications.
-Monitors systems and creates plans for responding to incidents, fixing issues, and preventing future problems.
- Uses tools such as version control, configuration management, cloud services, containers, service mesh, infrastructure coding, monitoring, logging, alerting, and more.
- Understand and follows the principles of site reliability engineering, such as embracing failure, automating everything, reducing toil, measuring everything, and balancing speed and stability.
 

Module 1: Introducing SRE

• Implementing SRE principles
• Implementing SRE practices
• Implementing SRE Models
• SRE Patterns and Anti -Patterns
• Use Case: IT Automation
• Video: What is Site Reliability

Module 2: On-Call

• What is on call?
• On Call Challenges
• On-Call Rotations
• On-call rotation best practices
• Benefits of on -call rotation
• On call SRE Team Building
• On-call Incentives
• Use Case: On-Call Team Structure
• Module Quiz
• Tool Demo: PagerDuty for AIOps Solution

Module 3: Managing Incident Response

• What is Incident Response?
• Incident Command System (ICS)
• Incident response Team
• Metrics for effective Incident Response
• Best Practices
• Use Case: Automatic Security
• Module Quiz
• TOOL Demo – Major Incident Management

Module 4: Blameless Incident Postmortem

• Incident postmortem
• Why Incident Postmortems
• Streamline the postmortem Process
• Blameless postmortem
• Incident Postmorten best Practices
• Use Case: Network Outage Playbook
• Module Quiz

Module 5: Data And CI/CD Pipeline

• What is a data pipeline?
• Data pipeline components
• Data pipeline types and use cases
• Implementation options for data pipelines
• Continuous Delivery Pipeline
• Data Pipelines On AWS, Microsotf Azure, GCP
• Use Case: Traditional analytics
• Use Case: Real-time analytics
• Use Case: ML Pipeline
• Module Quiz
• AWS CodePipeline Demo: Best Practices for Data Pipelines Release Process 

Module 6: MLOps

• Define MLOps
• Evolution of the MLOps
• ML/AI and MLOps capabilities
• Implement AI/ML
• AI/ML/MLOps Stack Canvas
• Deploy ML/AI
• MLOps Maturity Level
• MLOps Infrastructure Stack
• MLOps Principles
• Use Case
• Module Quiz
• Tool Demo: Data Versioning and Data Lineage With Pachyderm

Module 7: Deployment Strategies

• Deployment Strategies Defi ned
• Deployment Best Practices
• Deployment Use Cases
• Monolith Vs SOA Vs Microservices
• Tool Demo: Kubernetes Blue Green Deployment
• Module Quiz

Module 8: Observability

• What is Observability?
• Primary Data Classes of Observability
• Objectives of Observability
• Difference between Monitoring and Observability
• Synthetic Monitoring Vs Real User Monitoring
• Why Architectures Require Observability
• Building a Continuously Observable System
• Use and benefits of observability for SREs
• Enterprise Observability Strategy
• Measuring Organizations Observability
• Observability in containers and microservices
• Criteria for good observability tools
• Module Quiz
  Tool Overview: Elastic Observability

Bootcamp Syllabus