Hari Thapliyaal

Dr. Hari Thapliyal is a seasoned professional and prolific blogger with a multifaceted background that spans the realms of Data Science, Project Management, and Advait-Vedanta Philosophy. Holding a Doctorate in AI/NLP from SSBM (Geneva, Switzerland), Hari has earned Master’s degrees in Computers, Business Management, Data Science, and Economics, reflecting his dedication to continuous learning and a diverse skill set.

With over three decades of experience in management and leadership, Hari has proven expertise in training, consulting, and coaching within the technology sector. His extensive 16+ years in all phases of software product development are complemented by a decade-long focus on course design, training, coaching, and consulting in Project Management.

In the dynamic field of Data Science, Hari stands out with more than three years of hands-on experience in software development, training course development, training, and mentoring professionals. His areas of specialization include Data Science, AI, Computer Vision, NLP, complex machine learning algorithms, statistical modeling, pattern identification, and extraction of valuable insights.

Hari’s professional journey showcases his diverse experience in planning and executing multiple types of projects. He excels in driving stakeholders to identify and resolve business problems, consistently delivering excellent results. Beyond the professional sphere, Hari finds solace in long meditation, often seeking secluded places or immersing himself in the embrace of nature.

3 minute read

AI-Powered DevOps for AIOps

Program Outline: AI-Powered DevOps (AIOps)

Duration:

5 Days

Course Audience:

DevOps Leads, Support Leads, Support Managers

Target Unit:

Delivery CoE, Product, Internal Applications

Course Outline

Day 1: Introduction to AIOps

Morning Sessions 9:00 AM to 1:00 PM

1. Session 1 - Foundations of AIOps (9:00 AM - 10:30 AM)

  • What is AIOps?
  • Evolution of AI in DevOps
  • Key Benefits and Challenges

2. Session 2 - Core Concepts and Components of AIOps (10:45 AM - 1:00 PM)

  • Data Sources in AIOps (Logs, Metrics, Events)
  • Machine Learning and AI in DevOps

Lunch 1:00 PM to 2:00 PM

Afternoon Sessions 2:00 PM to 5:00 PM

3. Session 3 - AIOps Tools Overview (2:00 PM - 3:30 PM)

  • Popular AIOps Platforms (Splunk, Dynatrace, Moogsoft)
  • Overview of Open-Source AIOps Tools

4. Session 4 - Case Studies and Industry Examples (3:45 PM - 5:00 PM)

  • Real-World Success Stories of AIOps

Day 2: Data and Machine Learning for AIOps

Morning Sessions 9:00 AM to 1:00 PM

5. Session 5 - Data Preparation for AIOps (9:00 AM - 10:30 AM)

  • Collecting and Cleaning Data for Analysis
  • Importance of Data Quality

6. Session 6 - Machine Learning Models in AIOps (10:45 AM - 1:00 PM)

  • Predictive Models for Incident Management
  • Anomaly Detection Algorithms

Lunch 1:00 PM to 2:00 PM

Afternoon Sessions 2:00 PM to 5:00 PM

7. Session 7 - Hands-On: Building Anomaly Detection Models (2:00 PM - 3:30 PM)

  • Setting Up the Environment
  • Training and Evaluating Models

8. Session 8 - Challenges in Applying ML to DevOps (3:45 PM - 5:00 PM)

  • Model Drift and Retraining
  • Overcoming Data Silos

Day 3: AIOps for Monitoring and Incident Management

Morning Sessions 9:00 AM to 1:00 PM

9. Session 9 - AI for Continuous Monitoring (9:00 AM - 10:30 AM)

  • Monitoring Tools Powered by AI
  • Metrics to Monitor in DevOps

10. Session 10 - Incident Detection and Resolution with AIOps (10:45 AM - 1:00 PM)

  • Automated Root Cause Analysis
  • Proactive Incident Resolution

Lunch 1:00 PM to 2:00 PM

Afternoon Sessions 2:00 PM to 5:00 PM

11. Session 11 - Hands-On: Automating Alerts and Notifications (2:00 PM - 3:30 PM)

  • Setting Up Alerting Systems
  • Integrating AI for Prioritizing Alerts

12. Session 12 - Best Practices for AI-Driven Monitoring (3:45 PM - 5:00 PM)

  • Designing Scalable Monitoring Architectures
  • Ensuring Low False Positive Rates

Day 4: Automating DevOps Workflows with AIOps

Morning Sessions 9:00 AM to 1:00 PM

13. Session 13 - AI for CI/CD Pipelines (9:00 AM - 10:30 AM)

  • Optimizing Build and Deployment Processes
  • AI-Driven Code Quality Analysis

14. Session 14 - Automating Remediation and Rollbacks (10:45 AM - 1:00 PM)

  • Self-Healing Systems
  • Automated Rollbacks for Failures

Lunch 1:00 PM to 2:00 PM

Afternoon Sessions 2:00 PM to 5:00 PM

15. Session 15 - Hands-On: Automating a CI/CD Pipeline (2:00 PM - 3:30 PM)

  • Building AI-Powered CI/CD Workflows
  • Testing Deployment Automation

16. Session 16 - Governance and Compliance in AIOps (3:45 PM - 5:00 PM)

  • Ensuring Compliance with Automated Systems
  • AI for Audit Trails

Day 5: Scaling and Advancing AIOps

Morning Sessions 9:00 AM to 1:00 PM

17. Session 17 - Scaling AIOps Across the Organization (9:00 AM - 10:30 AM)

  • Integrating AIOps with Existing Systems
  • Cross-Team Collaboration with AIOps

18. Session 18 - Emerging Trends and Technologies in AIOps (10:45 AM - 1:00 PM)

  • AI Advancements Impacting DevOps
  • Exploring Future Use Cases

Lunch 1:00 PM to 2:00 PM

Afternoon Sessions 2:00 PM to 5:00 PM

19. Session 19 - Capstone Project: Building an End-to-End AIOps Workflow (2:00 PM - 4:00 PM)

  • Group Activity: Solve a DevOps Challenge Using AIOps
  • Presenting Solutions and Feedback

20. Session 20 - Wrap-Up and Next Steps (4:00 PM - 5:00 PM)

  • Recap of Key Learnings
  • Q&A and Certification Distribution

Key Outcomes:

  • Understand the principles and tools of AIOps.
  • Leverage AI to optimize monitoring, alerting, and incident resolution.
  • Automate CI/CD pipelines and remediation tasks.
  • Design scalable, AI-integrated DevOps workflows.
  • Familiarity with basic DevOps concepts and tools.
  • Review popular AIOps platforms and their features.
  • Explore examples of AI in IT operations.

Materials Provided:

  • Course slides and notes.
  • Access to trial AIOps tools.
  • Sample scripts and workflows.
  • Certificate of Completion.

Updated:

Leave a comment

You may also enjoy