AIOps Certification Training Course | AIOps
Certified Professional (AIOCP)

Introduction to AIOps:

• What is AIOps?

• Benefits of AIOps for IT operations.

• How AIOps helps in monitoring and managing IT environments.

• Role of Artificial Intelligence and Machine Learning in automating IT operations.

• Key use cases of AIOps.

Components of AIOps:

• Event Management

• Incident Management

• Automation and Self-Healing

• Root Cause Analysis (RCA)

• Predictive Analytics

AIOps in the Modern IT Ecosystem:

• Integration with existing IT operations.

• AIOps platforms overview (Moogsoft, BigPanda, etc.)

Real-world Case Studies of AIOps:

• How businesses have successfully implemented AIOps.

• Key challenges and best practices.

Introduction to Bash Scripting:

• Understanding the Linux terminal and basic commands.

• Basics of shell scripting.

Key Concepts in Bash Scripting:

• Variables, strings, and data types in Bash.

• Control flow in Bash: If-else, loops, and case statements.

• Functions in Bash.

• Handling user inputs and outputs.

File Operations:

• Creating, deleting, and modifying files with Bash.

• Reading and writing to files in Bash.

Automating System Tasks with Bash Scripts:

• Scheduled tasks using cron jobs.

• Backup automation, log file rotation.

Debugging and Testing Bash Scripts:

• Error handling in Bash.

• Best practices in writing clean and efficient Bash scripts.

Overview of AWS:

• Introduction to AWS cloud services.

• Benefits of AWS cloud computing.

Core AWS Services:

• EC2 (Elastic Compute Cloud)

• S3 (Simple Storage Service)

• IAM (Identity and Access Management)

• VPC (Virtual Private Cloud)

• Lambda (Serverless computing)

Architecting Solutions on AWS:

• Best practices for architecture on AWS.

• Multi-region and high-availability strategies.

• Using AWS Well-Architected Framework.

Security in AWS:

• Identity and Access Management (IAM)

• Security Best Practices

• Compliance in AWS

Monitoring and Optimization in AWS:

• CloudWatch, CloudTrail, and Cost Explorer.

• Autoscaling and load balancing in AWS.

Introduction to Docker:

• What is Docker and why is it important?

• Benefits of containers over traditional virtual machines.

• Components of Docker: Docker images, containers, and Dockerfile.

Docker Commands and Management:

• Docker CLI commands for managing containers and images.

• Building and running containers.

• Volumes and networks in Docker.

Docker Compose:

• Multi-container applications with Docker Compose.

• How to define, run, and scale multi-container Docker applications.

Best Practices in Docker:

• Creating efficient Docker images.

• Docker security best practices.

• Optimizing Docker workflows.

Overview of Jira & Confluence:

• What is Jira and how it supports Agile project management.

• Confluence as a collaborative documentation tool.

Setting up Jira for Project Management:

• How to create and manage projects in Jira.

• Creating and managing issues and sprints.

• Jira workflows and customizations.

Using Confluence for Collaboration:

• Creating and organizing documents in Confluence.

• Integration between Jira and Confluence.

• Collaborative editing and sharing of information.

Agile in Jira:

• Scrum and Kanban boards in Jira.

• Advanced Agile reporting and dashboards.

Introduction to Python for Backend Development:

• Why Python is a popular language for backend development.

• Python libraries and frameworks for web development.

Flask Framework for Web Applications:

• Flask setup and basic routing.

• Creating RESTful APIs with Flask.

• Using templates and forms in Flask.

MySQL Database Integration:

• Connecting Flask to MySQL using SQLAlchemy.

• CRUD operations in MySQL with Flask.

• Database migration in Flask using Flask-Migrate.

Flask Security Best Practices:

• Securing user authentication and authorization.

• Using Flask extensions for security.

Deploying Python/Flask Applications:

• Deployment on AWS, Heroku, or Docker.

Introduction to Git:

• What is Git and why version control is important?

• Git repositories and workflows.

Common Git Commands:

• Cloning, staging, committing, and pushing changes.

• Branching and merging strategies.

Using GitHub for Collaboration:

• Setting up repositories on GitHub.

• Pull requests and code reviews.

• GitHub Actions for CI/CD.

Overview of Kubernetes:

• What is Kubernetes and how it manages containers?

• Components of Kubernetes (Pods, Nodes, Services, etc.).

Kubernetes Cluster Management:

• Deploying and managing applications in Kubernetes.

• Scaling and updating applications in Kubernetes.

• Networking and service discovery in Kubernetes.

Introduction to Helm:

• What is Helm and how does it simplify Kubernetes management?

• Helm charts for application deployment.

• Installing and configuring Helm.

Introduction to Infrastructure as Code (IaC):

• What is Infrastructure as Code?

• Benefits of IaC and Terraform in managing cloud infrastructure.

Using Terraform:

• Writing and executing Terraform code.

• Terraform providers and modules.

• State management in Terraform.

Terraform Best Practices:

• Managing and versioning infrastructure.

• Managing state and remote backends.

Introduction to Continuous Integration (CI):

• What is CI and why it's important for DevOps.

• Key concepts in CI: Pipelines, Builds, and Deployments.

ArgoCD for GitOps:

• What is GitOps and how does ArgoCD implement it?

• Managing Kubernetes deployments with ArgoCD.

• ArgoCD best practices for continuous delivery.

Overview of Prometheus:

• What is Prometheus and why it’s used for monitoring?

• Key components of Prometheus: Metrics, scraping, and alerting.

Grafana for Data Visualization:

• Setting up Grafana for monitoring.

• Creating dashboards and visualizing Prometheus data.

• Alerts and automated responses using Grafana.

Introduction to Jupyter Notebooks:

• What is Jupyter Notebooks and its role in data science?

• Writing and running Python code within Jupyter.

Using Jupyter Notebooks for Data Analysis:

• Working with data, visualizations, and models.

• Sharing and collaborating with Jupyter Notebooks.

Overview of TensorFlow:

• What is TensorFlow and its applications in machine learning?

• TensorFlow’s features and architecture.

Building Models in TensorFlow:

• Data preprocessing and feature engineering in TensorFlow.

• Training neural networks using TensorFlow.

• Optimizing models for better performance.

Overview of PyTorch:

• What is PyTorch and how it compares to TensorFlow?

• Key features of PyTorch.

Using PyTorch for Model Development:

• Building and training models using PyTorch.

• Working with tensors and neural networks in PyTorch.

• Performance optimization in PyTorch.

Introduction to Pytest:

• What is Pytest and how to write tests in Python?

• Benefits of Pytest for unit testing.

Advanced Pytest Usage:

• Writing complex tests with fixtures.

• Parallel test execution in Pytest.

Overview of scikit-learn:

• What is scikit-learn and its role in machine learning?

• Key algorithms available in scikit-learn.

Model Evaluation with scikit-learn:

• Cross-validation and model selection in scikit-learn.

• Performance metrics for classification and regression.

Overview of KServe:

• What is KServe and its role in serving machine learning models?

• Integrating KServe with Kubernetes for scalable deployments.

Deploying Models with KServe:

• Deploying machine learning models as REST APIs using KServe.

• Managing different versions of models.

Introduction to Apache Airflow:

• What is Apache Airflow and how it manages workflows?

• Airflow DAGs (Directed Acyclic Graphs) and tasks.

Scheduling and Orchestrating Data Pipelines:

• Building complex data pipelines with Apache Airflow.

• Monitoring and error handling in Airflow.

What is TensorBoard?

• Introduction to TensorBoard and its role in monitoring machine learning experiments.

• Visualizing metrics and model performance using TensorBoard.

Introduction to Apache Kafka:

• What is Kafka and how it handles real-time data streams?

• Kafka’s core components: Brokers, topics, and consumers.

Data Pipeline Integration with Kafka:

• Setting up Kafka producers and consumers.

• Use cases of Kafka in event-driven architectures.

Introduction to Moogsoft:

• What is Moogsoft and its role in IT operations?

• Moogsoft for event correlation and noise reduction.

Setting Up Moogsoft for Incident Management:

• Automating event correlation and reducing false alerts.

• Integrating Moogsoft with other monitoring tools.

Introduction to PagerDuty:

• What is PagerDuty and how it helps in incident response?

• Key features of PagerDuty for automation and response.

Integrating PagerDuty with Monitoring Tools:

• Setting up alerts and automating incident response with PagerDuty.

Overview of Rundeck:

• What is Rundeck and its role in IT automation?

• Creating and automating tasks with Rundeck.

Self-Healing Automation with Rundeck:

• Building self-healing scripts for IT operations.

Elastic SIEM for Security Event Monitoring:

• What is Elastic SIEM and how it helps in threat detection?

• Setting up Elastic SIEM for log analysis and security monitoring.

SOAR Integration with Elastic SIEM:

• Automating responses to security incidents with Elastic SOAR.

Experimenting with Moogsoft for AIOps:

• Leveraging Moogsoft for real-time incident and event management.

• Hands-on with Moogsoft to automate root cause analysis and incident management.