AGENDA OF THE AIOPS CERTIFICATION TRAINING Download Curriculum
- 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
- Overview of Prometheus
- Brief history and purpose
- Key features and architecture
- Basic Installation and Configuration
- Quick setup guide
- Overview of configuration files and settings
- Understanding Metrics and Data Model
- Introduction to Prometheus metrics
- Data types and structure
- Q&A Session
Introduction to Prometheus
Basic Monitoring with Prometheus
- Instrumentation and Metrics Collection
- How to add Prometheus metrics to an application
- Best practices for metric collection
- Introduction to Prometheus Query Language (PromQL)
- Basic syntax and queries
- Creating simple alerts
- Hands-On Exercise
- Quick setup of basic monitoring for a demo application
Introduction to Grafana and Dashboard Creation
- Overview of Grafana
- Key features and integration with Prometheus
- Setting Up Grafana
- Connecting Grafana to Prometheus
- Creating Basic Dashboards in Grafana
- Introduction to dashboard creation and configuration
- Overview of visualization type
- Hands-On Exercise
- Participants create a basic dashboard for the demo application
Advanced Features and AIOps Integration
- Advanced Dashboard Techniques in Grafana
- Dynamic dashboards with variables
- Setting up basic alerts in Grafana
- Integrating Prometheus and Grafana with AIOps
- How these tools fit into an AIOps strategy
- Brief on AIOps concepts relevant to monitoring and observability
- Wrap-Up and Q&A
- Recap of key concepts
- Open floor for questions and discussion on real-world applications
Introduction to the ELK Stack
- Overview of ELK Stack
- Introduction to Elasticsearch, Logstash, and Kibana
- Role of ELK in AIOps
- Basic architecture and flow of data within the ELK Stack
- Introduction to Elasticsearch
- Understanding Elasticsearch basics: Indexes, Documents, and Nodes
- Basic Elasticsearch operations: CRUD (Create, Read, Update, Delete)
- Q&A Session
- Address initial queries and clarifications
Deep Dive into Logstash and Data Ingestion
- Understanding Logstash
- Logstash fundamentals: Input, Filter, and Output plugins
- Configuring Logstash for data ingestion
- Hands-On Exercise: Setting Up Logstash
- Walkthrough of setting up a basic Logstash pipeline
- Ingesting sample data into Elasticsearch
Kibana for Data Visualization and Analysis
- Introduction to Kibana
- Kibana Dashboard, Visualization, and Discover features
- Connecting Kibana to Elasticsearch
- Hands-On Exercise: Creating Visualizations and Dashboards
- Participants create basic visualizations and dashboards using the ingested data
- Exploration of Kibana's features relevant to AIOps
ELK Stack in AIOps and Advanced Topics
- ELK Stack in the Context of AIOps
- Integrating ELK with AIOps workflows
- Real-world use cases of ELK in AIOps (e.g., anomaly detection, performance monitoring)
- Advanced ELK Features
- Brief on advanced Elasticsearch queries
- Overview of X-Pack features (security, alerting, machine learning)
- Wrap-Up and Q&A
- Recap of key points
- Open Q&A session to discuss practical applications and address any remaining questions
Introduction to Kibana
- 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
- Setting up a basic Kafka environment
- Overview of Kafka configuration files
- Kafka Producers and Consumers
- Understanding Producers and Consumers
- Writing basic producers and consumers
- Q&A Session
- Address initial queries and clarifications
Kafka in Depth - Topics, Partitions, and Replication
- Deep Dive into Kafka Topics and Partitions
- Creating and managing Topics
- Understanding Partitions for scalability and reliability
- Kafka Replication and Fault Tolerance
- Concept of replication for high availability
- Leader and follower partitions
: Kafka Streams and Kafka Connect
- Introduction to Kafka Streams
- Understanding stream processing in Kafka
- Basics of Kafka Streams API
- Kafka Connect for Integration
- Overview of Kafka Connect
- Setting up connectors for data import/export
Kafka in AIOps and Practical Exercise
- Using Kafka in an AIOps Context
- 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
- Building a simple pipeline for data ingestion and processing
- Monitoring and managing Kafka performance
- Wrap-Up and Q&A Session
- Recap of key concepts and best practices
- Open floor for final questions and discussions
Introduction to TensorFlow and Machine Learning Basics
- Overview of TensorFlow
- Introduction to TensorFlow and its relevance in AIOps
- Core features and capabilities of TensorFlow
- Machine Learning Fundamentals
- Brief overview of machine learning concepts
- How TensorFlow supports machine learning operations
- Setting Up TensorFlow
- Installation and setup of TensorFlow
- Introduction to TensorFlow’s programming model
- Q&A Session
- Address initial queries and clarifications
TensorFlow Basics - Operations, Graphs, and Sessions
- TensorFlow Core Concepts
- Understanding Tensors, Operations, Graphs, and Sessions
- Building simple computation graphs
- Hands-On Exercise: Basic TensorFlow Operations
- Creating and executing a simple TensorFlow program
- Introduction to TensorFlow data types and operations
Building Machine Learning Models with TensorFlow
- Introduction to Neural Networks in TensorFlow
- Basic concepts of neural networks
- Building a simple neural network in TensorFlow
- Practical Exercise: Building a Basic ML Model
- Step-by-step construction of a machine learning model for a simple problem (e.g., regression or classification)
TensorFlow in AIOps and Advanced Topics
- TensorFlow in the Context of AIOps
- Discussing the role of TensorFlow in AIOps (e.g., anomaly detection, predictive maintenance)
- Real-world examples of TensorFlow applications in AIOps
- Advanced TensorFlow Features
- Overview of advanced features like TensorFlow Extended (TFX), Keras for deep learning, and distributed training
- Wrap-Up and Q&A Session
- Recap of key concepts and best practices
- Open floor for final questions and discussions on practical TensorFlow applications in AIOps
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
Data Analysis Basics in Jupyter Notebook
- Data Import and Manipulation
- Importing data from various sources (CSV, databases)
- Basic data manipulation using Pandas
- Hands-On Exercise: Working with Data
- Participants practice importing and manipulating a sample dataset
Advanced Data Analysis and Visualization
- Advanced Data Analysis Techniques
- Exploring more complex data manipulation and transformation
- Introduction to time series analysis relevant to AIOps
- Data Visualization in Jupyter
- Using Matplotlib and Seaborn for data visualization
- Creating plots and charts relevant to AIOps data (e.g., performance metrics)
Jupyter Notebooks in AIOps Context and Best Practices
- Applying Jupyter Notebooks in AIOps
- 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
- Tips for effective use of Jupyter Notebooks
- Overview of advanced features like JupyterLab, extensions
- Wrap-Up and Q&A Session
- Recap of key concepts and functionalities
- Open floor for final questions and in-depth discussions
Introduction to Ansible and Configuration Management
- Overview of Ansible
- Introduction to Ansible and its role in AIOps
- Key features and advantages of using Ansible for configuration management
- Ansible Architecture and Components
- Understanding Ansible architecture: Playbooks, Roles, Tasks, Modules, Inventory
- YAML syntax basics
- Setting Up Ansible
- Installation and basic setup of Ansible
- Setting up an inventory file
- Q&A Session
- Addressing initial queries and clarifications
Basic Playbooks and Ad-hoc Commands
- Writing Your First Ansible Playbook
- Creating a simple playbook
- Defining tasks and running the playbook
- Ansible Ad-hoc Commands
- Introduction to ad-hoc commands in Ansible
- Practical examples of common ad-hoc commands
Advanced Ansible Features
- Variables, Templates, and Roles
- Using variables and templates for dynamic configurations
- Organizing playbooks with roles
- Error Handling and Debugging
- Best practices for error handling in Ansible playbooks
- Using Ansible's debugging tools
Ansible in AIOps and Hands-On Exercise
- Applying Ansible in an AIOps Context
- 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
- Participants work on creating a basic pipeline using Ansible
- Automating a simple operational task relevant to AIOps
- Wrap-Up and Q&A Session
- Recap of key concepts and functionalities
- Open floor for final questions and in-depth discussions
Introduction to Terraform and Infrastructure as Code
- Overview of Terraform
- Introduction to Terraform and its role in infrastructure automation
- Key features and benefits of using Terraform in AIOps
- Terraform Basics
- Understanding Terraform's syntax and structure
- Core concepts: Providers, Resources, Variables, State
- Setting Up Terraform
- Installing Terraform
- Basic setup and configuration
- Q&A Session
- Addressing initial queries and clarifications
Writing Terraform Configuration
- Creating Your First Terraform Configuration
- Writing a basic Terraform configuration file
- Managing infrastructure as code
- Understanding Terraform Workflow
- The Terraform workflow: init, plan, apply, destroy
- Hands-on demo of managing a simple infrastructure
Advanced Terraform Concepts
- Modules and Remote State
- Using modules to organize and reuse code
- Managing state in complex environments
- Dynamic Infrastructure with Terraform
- Dynamic configurations with loops and conditionals
- Integrating with cloud providers (AWS, Azure, GCP)
Terraform in AIOps and Practical Exercise
- Terraform in an AIOps Context
- Real-world use cases of Terraform in AIOps
- Automating and maintaining AIOps infrastructure with Terraform
- Hands-On Exercise: Implementing an AIOps Scenario
- Participants implement a small-scale infrastructure setup relevant to AIOps
- Practicing Terraform commands and configurations
- Wrap-Up and Q&A Session
- Recap of key concepts and best practices
- Open floor for final questions and discussions on practical applications
Introduction to Jenkins and Continuous Integration
- Overview of Jenkins
- Introduction to Jenkins and its importance in CI/CD pipelines
- The role of Jenkins in AIOps
- Jenkins Architecture and Key Concepts
- Understanding Jenkins architecture: master, agents, plugins
- Core concepts: Jobs, Builds, Plugins, Pipelines
- Setting Up Jenkins
- Installing and configuring Jenkins
- Navigating the Jenkins interface
- Q&A Session
- Addressing initial queries and clarifications
Building Jobs and Basic Pipelines in Jenkins
- Creating Your First Jenkins Job
- Setting up a freestyle project
- Configuring source code management (SCM), build triggers, and build steps
- Introduction to Jenkins Pipelines
- Creating a basic pipeline using Jenkinsfile
- Pipeline syntax and scripted vs. declarative pipelines
Advanced Jenkins Usage and Integration
- Automated Testing and Notifications
- Integrating automated testing into Jenkins pipelines
- Configuring build notifications (e.g., email, Slack)
- Integrating Jenkins with Other Tools
- Connecting Jenkins with version control systems (like Git)
- Using Jenkins with containerization tools (like Docker)
Jenkins in AIOps and Practical Exercise
- Jenkins in the Context of AIOps
- 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
- Participants create a simple CI/CD pipeline relevant to AIOps
- Emphasizing on automated deployment and testing
- Wrap-Up and Q&A Session
- Recap of key concepts and functionalities
- Open floor for final questions and discussions
Introduction to Rundeck and Runbook Automation
- Overview of Rundeck
- Introduction to Rundeck and its significance in AIOps
- Understanding the role of runbook automation in IT operations
- Rundeck Architecture and Key Features
- Core components: Jobs, Nodes, Projects, Commands
- Overview of Rundeck’s UI and basic navigation
- Setting Up Rundeck
- Installation and basic configuration
- Setting up projects and access controls
- Q&A Session
- Addressing initial queries and clarifications
Creating and Managing Jobs in Rundeck
- Defining and Executing Jobs
- Creating your first job in Rundeck
- Configuring job workflows, options, and scheduling
- Advanced Job Features
- Using job plugins for extended functionality
- Handling job outputs and logs
Integrating Rundeck with Other Tools and Services
- Rundeck Integrations
- Integrating with version control systems (e.g., Git)
- Connecting Rundeck with monitoring tools (e.g., Nagios, Splunk)
- API and CLI Usage
- Utilizing Rundeck’s API for automation
- Command-line interface for Rundeck management
Rundeck in AIOps and Practical Exercise
- Applying Rundeck in an AIOps Context
- Case studies or examples of Rundeck used in AIOps scenarios
- Automating routine operations and incident response
- Hands-On Exercise: Implementing a Runbook Automation Scenario
- Participants implement a basic runbook automation task relevant to AIOps
- Emphasizing on automated problem resolution and reporting
- Wrap-Up and Q&A Session
- Recap of key concepts and functionalities
- Open floor for final questions and discussions on practical applications
What is difference between DevOps and AIOps?
| SL | DevOps | AIOps |
|---|---|---|
| 1 | Combination of a cultural philosophies, practices, and tools | Multi-layered technology platforms that automates and enhances IT operations |
| 2 | Delivers applications and services at high velocity | Eliminates human errors and saves time |
| 3 | Enables fast releases and delpoyment cycles by agile development methdologies | Automtically spots and reacts to issues in real time and automation smart DevOps and CloudOps |
| 4 | Operations collaborate with deveopments to monitor self-service solutions | Key components incude Big Data and Machine Learning |
| 5 | Principles used by DevOps include holistic system thinking, no silos, rapid, useful feedback and automate drudgery away | Thr priciple used by AIOps include taking inputs from existing monitoring tools, applying algorithmic techniques, analysing them, and producing an output that is an insight action item for the operations team. |
OUR COURSE IN COMPARISON
| FEATURES | DEVOPSSCHOOL | OTHER |
|---|---|---|
| Lifetime Technical Support | ||
| Lifetime LMS access | ||
| Industry recognized certification | ||
| Group Discounts |
As digital services become the new norm, clients/consumers/users now have multiple options and are increasingly prepared to walk away if they will face and problems or issues with your product.
In the modern process of development, digital services often contain hundreds of millions of lines of code and billions of dependencies and with multicloud, open source environments, tangled web of interconnected cloud platforms, microservices, containers, serverless architecture, and orchestration platforms makes it more complicated to manage effectively in real time when it takes just a small issue in a single line of code to trigger a storm of alerts.
It is nearly impossible for the tradictional operations teams to manually find the root cause of a problem that affects multiple services and raises thousands of events per second. In response, AI and machine learning have emerged as a means to relieve some of the manual intervention required. In the journey towards digital transformation, AIOps can make life better of your engineers and in the same time it can benefit the organization by reducing their cloud costs, improve cloud security compliance, and reduce alerts fatigue is by bringing intelligent machine operations through AIOps.
- According to Gartner, 30% of the large companies will be using artificial intelligence for IT operations (AIOps) by 2023.
- Enterprises who wants to not only survive but succeed in today’s digital economy must take into consideration the use of AI in IT operations.
- Official Docker certfication recognized and acceptable worldwide.
- AIOps market experienced a growth of 87% in recent 3 years - from 1.73 billion in 2017 to 13.51 billion USD in 2020. And recent researhces also predicts that AIOps the will experience further 21.05% growth between 2021 and 2026, reaching up to USD 40.91 billion in market size.
- DevOpsSchool is an industry leader in delivering DevOps, Cloud and Container training programs since 2014. And Docker is a container tool.
- DevOpsSchool is recognized as one of the best reviewed and top rated DevOps, Cloud, Artificial Inteligence and Machine learning training and certification institute.
- DevOpsSchool has designed AIOps training program according to the industries current problems and solutions based requirement.
- DevOpsSchool facilitate regular, weekdays, weekends and customized AIOps training programs.
- DevOpsSchool has best AIOps trainers and mentors with 10 to 17 years of real industry experience.
- DevOpsSchool’s offers the best AIOps training and support with well-defined training modules and AIOps platforms sessions.
- We have all kind of AIOps programs available i.e group training, public batches, corporate sessions, One-on-One sessions are available too.
- We give our candidates 24×7 AIOps Learning Management System (LMS) access. Students are free to access all the AIOps learning materials – unlimited number of hours as per their own preferred timings.
- We also provide AIOps technical support even after completion of training.
- Variety of AIOps study materials available: PDF slides, Video Tutorials, Notes, PPTs and Real time scenario based projects and assignments.
- Access to group discussions, interview preparation KIT – Interview Questions (Technical and HR), Lab Guides
- Globally recognized AIOps course completion certification.
- The ability to retake the AIOPs classes at no-charge as often as desired.
- Helps participants to take knowledge of complex AIOps technical concepts.
- Allow access to the AIOps support Team is for a lifetime and will be available 24/7. The team will help you in resolving queries, during and after the training.
- After AIOps training a participant can self-assessed using our self-assement ecosystem feature in our LMS.
- well-suited for: Software developers, Software engineers, Technical leads, System administrators, AI Engieers, DevOps engineers, SRE Engieers.
- Infrastructure Managment Knowledge
