Table of Contents

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.

This course will teach you the essentials of Artificial Intelligence for IT Operations (AIOps) to help you assess how you can apply it to the IT operations function of your organization to enable managing it more effectively and efficiently.

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

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

Benefits of Artificial Intelligence for IT Operations (AIOps)
Artificial Intelligence for IT Operations (AIOps) Overview
Benefits of AIOps
Use Case: Evaluating the Benefits of AIOps
Implications of AIOps for Business
Implications of AIOps for Business
Use Case: Implications of AIOps for Business
Key Capabilities of Artificial Intelligence for IT Operations (AIOps)
Key Capabilities of AIOps
Use Case: Understanding Key Capabilities of AIOps
Key Dimensions of IT Operations Monitoring
IT Operations Monitoring: Overview and Relevance
Understanding Key Dimensions of IT Operations Monitoring
Key Dimensions of IT Operations Monitoring and AIOps
Use Case: Understanding Key Dimensions of IT Operations Monitoring
AIops Deployment Types & storages
AIops Industry Use cases
AIOps Vs DevOps Vs MLOps Life cycle
AIOps Challenges
AIOps Popular Solutions
AIOps Best Practices
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

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Rajesh Kumar

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