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
- 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