Introduction

The “AiOps Certified Professional” (AIOCP) designation typically refers to a certification program aimed at individuals who want to demonstrate their expertise in the field of Artificial Intelligence for IT Operations (AIOps). This certification would likely cover a range of topics pertinent to AIOps, including but not limited to machine learning, data analytics, automation, monitoring, and incident response within IT operations.

Purpose of AIOCP Certification:

• Validate Skills: It serves to validate the skills and knowledge of professionals in using AI technologies and practices to improve IT operations.
• Industry Recognition: It provides recognition within the industry, indicating a professional level of competency in the AIOps domain.

Potential Content of AIOCP Program:

• Fundamentals of AI and Machine Learning: Understanding the basics of AI and ML, how they apply to IT operations.
• Data Management and Analysis: Techniques for managing and analyzing large volumes of IT data.
• Automation in IT Operations: Using AI to automate routine tasks, incident responses, and workflow optimization.
• Monitoring and Observability: Implementing AI-driven monitoring tools and practices for better visibility into IT systems.
• Incident Management and Response: Leveraging AI for quicker and more effective incident resolution.
• Integration of AI Tools and Platforms: Best practices for integrating various AI tools (like machine learning libraries, monitoring tools, etc.) into IT environments.
• Case Studies and Real-World Applications: Learning from real-world scenarios and case studies where AIOps has been successfully implemented.

Target Audience:

• IT professionals, system administrators, and operations engineers seeking to integrate AI into their workflows.
• Professionals aiming to specialize in the field of AIOps.
• Teams and organizations looking to enhance their IT operations with AI technologies.

Format and Requirements:

• The program may include a mix of theoretical learning, practical exercises, and case studies.
• It might require passing an examination that tests the candidate’s knowledge and understanding of AIOps principles and practices.

Benefits of AIOCP:

• Professional Growth: Enhances career opportunities and professional growth in the rapidly evolving field of IT operations.
• Skills Enhancement: Helps in staying current with the latest AI technologies and practices in IT operations.
• Organizational Impact: Enables professionals to contribute more effectively to their organizations by optimizing IT operations through AI.

What you’ll learn

• AIOps Foundations
• AIOps Implementation Roadmap
• AIOps Project workflow
• AIops Deployment Types & storages
• AIops Industry Use cases
• AIOps Vs DevOps Vs MLops Lifec cycle
• AIOps Popular Solutions
• AIOps Challenges
• AIops Tools
• AIOps Best Practices
• AIOps supporting DevOps & SRE

Day 1: Understanding of AiOps

Half Day: Overview of AiOps

1. Benefits of Artificial Intelligence for IT Operations (AIOps)                          
2. Artificial Intelligence for IT Operations (AIOps) Overview              
3. Benefits of AIOps                          
4. Use Case: Evaluating the Benefits of AIOps                        
5. Implications of AIOps for Business                         
6. Implications of AIOps for Business                         
7. Use Case: Implications of AIOps for Business                     
8. Key Capabilities of Artificial Intelligence for IT Operations (AIOps)                            
9. Key Capabilities of AIOps                           
10. Use Case: Understanding Key Capabilities of AIOps                        
11. Key Dimensions of IT Operations Monitoring                     
12. IT Operations Monitoring: Overview and Relevance                       
13. Understanding Key Dimensions of IT Operations Monitoring                      
14. Key Dimensions of IT Operations Monitoring and AIOps                
15. Use Case: Understanding Key Dimensions of IT Operations Monitoring
16. AIops Deployment Types & storages
17. AIops Industry Use cases
18. AIOps Vs DevOps Vs MLOps Life cycle
19. AIOps Challenges
20. AIOps Popular Solutions
21. AIOps Best Practices
22. AIOps supporting DevOps & SRE

Second Half: Metrics collection: Prometheus, Grafana

Hour 1: Introduction to Prometheus

• Overview of Prometheus (15 mins)

• Brief history and purpose
• Key features and architecture

• Basic Installation and Configuration (15 mins)

• Quick setup guide
• Overview of configuration files and settings

• Understanding Metrics and Data Model (15 mins)

• Introduction to Prometheus metrics
• Data types and structure

• Q&A Session (15 mins)

Hour 2: Basic Monitoring with Prometheus

• Instrumentation and Metrics Collection (20 mins)

• How to add Prometheus metrics to an application
• Best practices for metric collection

• Introduction to Prometheus Query Language (PromQL) (20 mins)

• Basic syntax and queries
• Creating simple alerts

• Hands-On Exercise (20 mins)

• Quick setup of basic monitoring for a demo application

Hour 3: Introduction to Grafana and Dashboard Creation

• Overview of Grafana (15 mins)

• Key features and integration with Prometheus

• Setting Up Grafana (15 mins)

• Connecting Grafana to Prometheus

• Creating Basic Dashboards in Grafana (15 mins)

• Introduction to dashboard creation and configuration
• Overview of visualization types

• Hands-On Exercise (15 mins)

• Participants create a basic dashboard for the demo application

Hour 4: Advanced Features and AIOps Integration

• Advanced Dashboard Techniques in Grafana (20 mins)

• Dynamic dashboards with variables
• Setting up basic alerts in Grafana

• Integrating Prometheus and Grafana with AIOps (20 mins)

• How these tools fit into an AIOps strategy
• Brief on AIOps concepts relevant to monitoring and observability

• Wrap-Up and Q&A (20 mins)

• Recap of key concepts
• Open floor for questions and discussion on real-world applications

Day 2:  Data Collection and Monitoring Tools

 

First Half: Log management: ELK Stack (Elasticsearch, Logstash, Kibana)

Hour 1: Introduction to the ELK Stack

• Overview of ELK Stack (15 mins)

• Introduction to Elasticsearch, Logstash, and Kibana
• Role of ELK in AIOps
• Basic architecture and flow of data within the ELK Stack

• Introduction to Elasticsearch (15 mins)

• Understanding Elasticsearch basics: Indexes, Documents, and Nodes
• Basic Elasticsearch operations: CRUD (Create, Read, Update, Delete)

• Q&A Session (15 mins)

• Address initial queries and clarifications

Hour 2: Deep Dive into Logstash and Data Ingestion

• Understanding Logstash (20 mins)

• Logstash fundamentals: Input, Filter, and Output plugins
• Configuring Logstash for data ingestion

• Hands-On Exercise: Setting Up Logstash (20 mins)

• Walkthrough of setting up a basic Logstash pipeline
• Ingesting sample data into Elasticsearch

Hour 3: Kibana for Data Visualization and Analysis

• Introduction to Kibana (20 mins)

• Kibana Dashboard, Visualization, and Discover features
• Connecting Kibana to Elasticsearch

• Hands-On Exercise: Creating Visualizations and Dashboards (20 mins)

• Participants create basic visualizations and dashboards using the ingested data
• Exploration of Kibana’s features relevant to AIOps

Hour 4: ELK Stack in AIOps and Advanced Topics

• ELK Stack in the Context of AIOps (20 mins)

• Integrating ELK with AIOps workflows
• Real-world use cases of ELK in AIOps (e.g., anomaly detection, performance monitoring)

• Advanced ELK Features (20 mins)

• Brief on advanced Elasticsearch queries
• Overview of X-Pack features (security, alerting, machine learning)

• Wrap-Up and Q&A (20 mins)

• Recap of key points
• Open Q&A session to discuss practical applications and address any remaining questions

Second Half: Event streaming: Kafka

Hour 1: Introduction to Apache Kafka

• Overview of Kafka (15 mins)

• What is Apache Kafka and why it’s important in AIOps
• Kafka’s architecture and core components (Brokers, Topics, Producers, Consumers)

• Kafka Installation and Basic Configuration (15 mins)

• Setting up a basic Kafka environment
• Overview of Kafka configuration files

• Kafka Producers and Consumers (15 mins)

• Understanding Producers and Consumers
• Writing basic producers and consumers

• Q&A Session (15 mins)

• Address initial queries and clarifications

Hour 2: Kafka in Depth – Topics, Partitions, and Replication

• Deep Dive into Kafka Topics and Partitions (20 mins)

• Creating and managing Topics
• Understanding Partitions for scalability and reliability

• Kafka Replication and Fault Tolerance (20 mins)

• Concept of replication for high availability
• Leader and follower partitions

Hour 3: Kafka Streams and Kafka Connect

• Introduction to Kafka Streams (20 mins)

• Understanding stream processing in Kafka
• Basics of Kafka Streams API

• Kafka Connect for Integration (20 mins)

• Overview of Kafka Connect
• Setting up connectors for data import/export

Hour 4: Kafka in AIOps and Practical Exercise

• Using Kafka in an AIOps Context (20 mins)

• Role of Kafka in event-driven architectures for AIOps
• Real-world use cases: Log aggregation, metrics collection, real-time analytics

• Hands-On Exercise: Setting Up a Kafka Pipeline (20 mins)

• Building a simple pipeline for data ingestion and processing
• Monitoring and managing Kafka performance

• Wrap-Up and Q&A Session (20 mins)

• Recap of key concepts and best practices
• Open floor for final questions and discussions

Day 3: Data Collection and Monitoring Tools

First Half: Machine learning libraries: TensorFlow

Hour 1: Introduction to TensorFlow and Machine Learning Basics

• Overview of TensorFlow (15 mins)

• Introduction to TensorFlow and its relevance in AIOps
• Core features and capabilities of TensorFlow

• Machine Learning Fundamentals (15 mins)

• Brief overview of machine learning concepts
• How TensorFlow supports machine learning operations

• Setting Up TensorFlow (15 mins)

• Installation and setup of TensorFlow
• Introduction to TensorFlow’s programming model

• Q&A Session (15 mins)

• Address initial queries and clarifications

Hour 2: TensorFlow Basics – Operations, Graphs, and Sessions

• TensorFlow Core Concepts (20 mins)

• Understanding Tensors, Operations, Graphs, and Sessions
• Building simple computation graphs

• Hands-On Exercise: Basic TensorFlow Operations (20 mins)

• Creating and executing a simple TensorFlow program
• Introduction to TensorFlow data types and operations

Hour 3: Building Machine Learning Models with TensorFlow

• Introduction to Neural Networks in TensorFlow (20 mins)

• Basic concepts of neural networks
• Building a simple neural network in TensorFlow

• Practical Exercise: Building a Basic ML Model (20 mins)

• Step-by-step construction of a machine learning model for a simple problem (e.g., regression or classification)

Hour 4: TensorFlow in AIOps and Advanced Topics

• TensorFlow in the Context of AIOps (20 mins)

• Discussing the role of TensorFlow in AIOps (e.g., anomaly detection, predictive maintenance)
• Real-world examples of TensorFlow applications in AIOps

• Advanced TensorFlow Features (20 mins)

• Overview of advanced features like TensorFlow Extended (TFX), Keras for deep learning, and distributed training

• Wrap-Up and Q&A Session (20 mins)

• Recap of key concepts and best practices
• Open floor for final questions and discussions on practical TensorFlow applications in AIOps

Second Half: Data analysis tools: Jupyter Notebook

Hour 1: Introduction to Jupyter Notebooks

• Overview of Jupyter Notebooks (15 mins)

• Introduction to Jupyter Notebooks and their importance in data analysis
• Key features and benefits in the context of AIOps

• Setting Up Jupyter Notebooks (15 mins)

• Installation and basic setup
• Navigating the Jupyter Notebook interface

• Basic Operations in Jupyter Notebook (15 mins)

• Creating and managing notebooks
• Overview of Markdown, code cells, and kernel management

• Q&A Session (15 mins)

• Addressing initial queries and clarifications

Hour 2: Data Analysis Basics in Jupyter Notebook

• Data Import and Manipulation (20 mins)

• Importing data from various sources (CSV, databases)
• Basic data manipulation using Pandas

• Hands-On Exercise: Working with Data (20 mins)

• Participants practice importing and manipulating a sample dataset

Hour 3: Advanced Data Analysis and Visualization

• Advanced Data Analysis Techniques (20 mins)

• Exploring more complex data manipulation and transformation
• Introduction to time series analysis relevant to AIOps

• Data Visualization in Jupyter (20 mins)

• Using Matplotlib and Seaborn for data visualization
• Creating plots and charts relevant to AIOps data (e.g., performance metrics)

Hour 4: Jupyter Notebooks in AIOps Context and Best Practices

• Applying Jupyter Notebooks in AIOps (20 mins)

• Case studies or examples of Jupyter Notebooks used in AIOps scenarios
• Integrating Jupyter Notebooks with other AIOps tools and platforms

• Best Practices and Advanced Features (20 mins)

• Tips for effective use of Jupyter Notebooks
• Overview of advanced features like JupyterLab, extensions

• Wrap-Up and Q&A Session (20 mins)

• Recap of key concepts and functionalities
• Open floor for final questions and in-depth discussions

Day 4:  Analysis and Automation

First Half: Configuration management tools: Ansible

Hour 1: Introduction to Ansible and Configuration Management

• Overview of Ansible (15 mins)

• Introduction to Ansible and its role in AIOps
• Key features and advantages of using Ansible for configuration management

• Ansible Architecture and Components (15 mins)

• Understanding Ansible architecture: Playbooks, Roles, Tasks, Modules, Inventory
• YAML syntax basics

• Setting Up Ansible (15 mins)

• Installation and basic setup of Ansible
• Setting up an inventory file

• Q&A Session (15 mins)

• Addressing initial queries and clarifications

Hour 2: Basic Playbooks and Ad-hoc Commands

• Writing Your First Ansible Playbook (20 mins)

• Creating a simple playbook
• Defining tasks and running the playbook

• Ansible Ad-hoc Commands (20 mins)

• Introduction to ad-hoc commands in Ansible
• Practical examples of common ad-hoc commands

Hour 3: Advanced Ansible Features

• Variables, Templates, and Roles (20 mins)

• Using variables and templates for dynamic configurations
• Organizing playbooks with roles

• Error Handling and Debugging (20 mins)

• Best practices for error handling in Ansible playbooks
• Using Ansible’s debugging tools

Hour 4: Ansible in AIOps and Hands-On Exercise

• Applying Ansible in an AIOps Context (20 mins)

• Case studies or examples of Ansible used in AIOps scenarios
• Integration of Ansible with monitoring and alerting tools

• Hands-On Exercise: Building an AIOps Pipeline (20 mins)

• Participants work on creating a basic pipeline using Ansible
• Automating a simple operational task relevant to AIOps

• Wrap-Up and Q&A Session (20 mins)

• Recap of key concepts and functionalities
• Open floor for final questions and in-depth discussions

Second Half:  Infrastructure-as-code software tool: Terraform

Hour 1: Introduction to Terraform and Infrastructure as Code

• Overview of Terraform (15 mins)

• Introduction to Terraform and its role in infrastructure automation
• Key features and benefits of using Terraform in AIOps

• Terraform Basics (15 mins)

• Understanding Terraform’s syntax and structure
• Core concepts: Providers, Resources, Variables, State

• Setting Up Terraform (15 mins)

• Installing Terraform
• Basic setup and configuration

• Q&A Session (15 mins)

• Addressing initial queries and clarifications

Hour 2: Writing Terraform Configuration

• Creating Your First Terraform Configuration (20 mins)

• Writing a basic Terraform configuration file
• Managing infrastructure as code

• Understanding Terraform Workflow (20 mins)

• The Terraform workflow: init, plan, apply, destroy
• Hands-on demo of managing a simple infrastructure

Hour 3: Advanced Terraform Concepts

• Modules and Remote State (20 mins)

• Using modules to organize and reuse code
• Managing state in complex environments

• Dynamic Infrastructure with Terraform (20 mins)

• Dynamic configurations with loops and conditionals
• Integrating with cloud providers (AWS, Azure, GCP)

Hour 4: Terraform in AIOps and Practical Exercise

• Terraform in an AIOps Context (20 mins)

• Real-world use cases of Terraform in AIOps
• Automating and maintaining AIOps infrastructure with Terraform

• Hands-On Exercise: Implementing an AIOps Scenario (20 mins)

• Participants implement a small-scale infrastructure setup relevant to AIOps
• Practicing Terraform commands and configurations

• Wrap-Up and Q&A Session (20 mins)

• Recap of key concepts and best practices
• Open floor for final questions and discussions on practical applications

Day 5: CI/CD and Automation

First Half: Continuous integration tools: Jenkins

Hour 1: Introduction to Jenkins and Continuous Integration

• Overview of Jenkins (15 mins)

• Introduction to Jenkins and its importance in CI/CD pipelines
• The role of Jenkins in AIOps

• Jenkins Architecture and Key Concepts (15 mins)

• Understanding Jenkins architecture: master, agents, plugins
• Core concepts: Jobs, Builds, Plugins, Pipelines

• Setting Up Jenkins (15 mins)

• Installing and configuring Jenkins
• Navigating the Jenkins interface

• Q&A Session (15 mins)

• Addressing initial queries and clarifications

Hour 2: Building Jobs and Basic Pipelines in Jenkins

• Creating Your First Jenkins Job (20 mins)

• Setting up a freestyle project
• Configuring source code management (SCM), build triggers, and build steps

• Introduction to Jenkins Pipelines (20 mins)

• Creating a basic pipeline using Jenkinsfile
• Pipeline syntax and scripted vs. declarative pipelines

Hour 3: Advanced Jenkins Usage and Integration

• Automated Testing and Notifications (20 mins)

• Integrating automated testing into Jenkins pipelines
• Configuring build notifications (e.g., email, Slack)

• Integrating Jenkins with Other Tools (20 mins)

• Connecting Jenkins with version control systems (like Git)
• Using Jenkins with containerization tools (like Docker)

Hour 4: Jenkins in AIOps and Practical Exercise

• Jenkins in the Context of AIOps (20 mins)

• Discussing the role of Jenkins in automated operations
• Use cases of Jenkins in monitoring, alerting, and auto-remediation

• Hands-On Exercise: Implementing a CI/CD Pipeline (20 mins)

• Participants create a simple CI/CD pipeline relevant to AIOps
• Emphasizing on automated deployment and testing

• Wrap-Up and Q&A Session (20 mins)

• Recap of key concepts and functionalities
• Open floor for final questions and discussions

Second Half: Runbook Automation Platform: Rundeck

Hour 1: Introduction to Rundeck and Runbook Automation

• Overview of Rundeck (15 mins)

• Introduction to Rundeck and its significance in AIOps
• Understanding the role of runbook automation in IT operations

• Rundeck Architecture and Key Features (15 mins)

• Core components: Jobs, Nodes, Projects, Commands
• Overview of Rundeck’s UI and basic navigation

• Setting Up Rundeck (15 mins)

• Installation and basic configuration
• Setting up projects and access controls

• Q&A Session (15 mins)

• Addressing initial queries and clarifications

Hour 2: Creating and Managing Jobs in Rundeck

• Defining and Executing Jobs (20 mins)

• Creating your first job in Rundeck
• Configuring job workflows, options, and scheduling

• Advanced Job Features (20 mins)

• Using job plugins for extended functionality
• Handling job outputs and logs

Hour 3: Integrating Rundeck with Other Tools and Services

• Rundeck Integrations (20 mins)

• Integrating with version control systems (e.g., Git)
• Connecting Rundeck with monitoring tools (e.g., Nagios, Splunk)

• API and CLI Usage (20 mins)

• Utilizing Rundeck’s API for automation
• Command-line interface for Rundeck management

Hour 4: Rundeck in AIOps and Practical Exercise

• Applying Rundeck in an AIOps Context (20 mins)

• Case studies or examples of Rundeck used in AIOps scenarios
• Automating routine operations and incident response

• Hands-On Exercise: Implementing a Runbook Automation Scenario (20 mins)

• Participants implement a basic runbook automation task relevant to AIOps
• Emphasizing on automated problem resolution and reporting

• Wrap-Up and Q&A Session (20 mins)

• Recap of key concepts and functionalities
• Open floor for final questions and discussions on practical applications