Juniper Mist AIOps
- Duration: 3 Days (24 Hours)
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Prerequisites
Juniper Mist AIOps Training Course Overview
In this comprehensive three-day course, participants will delve into the world of Mist AI, exploring both resource-based and real-time event-based data. The course focuses on effectively accessing and searching this data through the Mist UI using Marvis. Additionally, participants will explore automation and integration possibilities leveraging the Mist APIs. Through a combination of hands-on labs and demonstrations, students will gain valuable experience with the various features of Mist AI.
Intended Audience For Juniper Mist AIOps Training
- Network Engineers
- IT Professionals
- Network Administrators
- Operations Staff
- Those Involved in Network Monitoring and Troubleshooting
- IT Managers
- Anyone Responsible for Mist AI Solutions
Learning objectives for the Juniper Mist AIOps training course
- Describe the data available in the Mist Cloud
- Describe Marvis components and operations
- Leverage Marvis to access Mist AI data
- Explain the built-in integration options
- Describe Mist RESTful API features and limitations
- Describe Mist WebSockets API features and limitations
- Describe Mist Webhook API features and limitations
- Perform Mist AI Operations using Postman
- Perform Mist AI Operations using Node-RED
- Explore Mist API using Python
- Perform advanced Mist AI automation using Python
- Describe 802.1X Authentication and operations
- Perform RADIUS server integration and role-based policy configuration
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Benefits of taking the Juniper Mist AIOps training
- Enhanced Troubleshooting Skills: Acquire advanced skills in network troubleshooting and gain insights into resolving issues effectively using Mist AI technologies.
- Optimized Network Performance: Learn to leverage Mist AIOps tools for proactive monitoring, ensuring optimal network performance, and addressing potential issues before they impact operations.
- Efficient Network Management: Explore automation capabilities that streamline network management tasks, saving time and resources while maintaining a high level of efficiency.
- Improved User Experience: Understand how Mist AIOps contributes to enhancing the end-user experience by providing a more reliable and responsive network infrastructure.
- Advanced Analytics: Gain proficiency in using Mist AI analytics to extract valuable insights from network data, enabling data-driven decision-making and strategic planning.
- Increased Operational Agility: Learn to adapt to changing network requirements swiftly and efficiently, promoting greater agility in responding to dynamic business needs.
- Optimal Resource Utilization: Acquire skills to optimize the utilization of networking resources, ensuring that infrastructure components are efficiently used to meet organizational objectives.
- Up-to-Date Industry Knowledge: Stay abreast of the latest advancements in AI-driven networking solutions, positioning yourself as a knowledgeable professional in the rapidly evolving field of AIOps.
- Certification: Obtain recognition for your proficiency in Mist AIOps through certification, enhancing your credibility and opening up opportunities for career advancement.
- Future-Ready Skills: Equip yourself with skills aligned with the latest trends in networking and artificial intelligence, making you well-prepared for the future demands of the IT industry.
Juniper Mist AIOps Training Course Modules
Module 1: Course Introduction
- Overview of the training program.
Module 2: What Is AIOps?
- Definition of AI and ML terminology.
- Explanation of AIOps goals.
- Importance of data in AIOps.
- Overview of Mist Cloud components.
Module 3: Mist AI Data
- Description of various Mist AI data types.
- Access Point (AP) Data
- LLDP Data
- Switch Data
- Config Data—JSON
- Event Data
- Insight Data
- Client Stats
- AP Stats
Module 4: RESTful API
- Definition of RESTful API.
- Building RESTful API requests.
- Features available through the RESTful API.
- Limitations of the Mist RESTful API.
Module 5: Postman
- Explanation of Postman.
- Interaction of Postman with the Mist API.
- Using Postman to automate tasks.
- Setting up a Postman environment.
- Utilizing the Juniper Mist Collection within Postman.
- Lab 1: Automating Mist AI Operations using Postman
- Lab 2: Mist Runner Collection
Module 6: Marvis
- Description of Marvis natural language queries.
- Marvis query language queries.
- Marvis Conversational Interface.
- Explanation of Marvis Actions.
Module 7: Marvis Data
- Overview of Marvis Client and Roaming data.
- Accessing and querying Mist data.
- How Marvis utilizes Mist data.
Module 8: Mist WebSocket API
- Definition of Webhook API.
- Using the Mist Webhook API.
- Features available via the Webhook API used by Mist.
- Limitations of the Mist Webhook API.
Module 9: Webhook API
- Definition of Webhook API.
- Mist Webhook API usage.
- Features available via the Webhook API.
- Limitations of the Mist Webhook API.
Module 10: Node-RED
- Explanation of Node-RED.
- Using Node-RED to interact with the Mist API.
- Solving use cases with Node-RED and the Mist API.
- Lab: Interacting with Mist API using Node-RED.
Module 11: Python and Mist API
- Definition of Python in network automation.
- Interacting with the Mist API using Python.
- Building Python scripts for Mist API interactions.
- Lab 3: Mist Operations Using Python
Module 12: Built-In Integration
- Ekahau and iBwave Import explanation.
- CloudShark integration.
- Integrating external captive portals.
- Demo: Building In Integration
Module 13: Python Automation
- Leveraging Python for automation.
- Types of automation possible with Python.
- Review of automation use cases and examples.
- Building Python scripts for Mist APIs.
- Lab 4: Python Automation
Module 14: 802.1X Authentication
- Components of AAA.
- Explanation of 802.1X operations.
- EAP operations.
- Different EAP types and their differences.
- RADIUS protocol and server.
- RADIUS attributes and their usage.
Module 15: RADIUS Integration
- Integrating a third-party RADIUS server into Mist.
- Steps to integrate ClearPass with Mist.
- Mapping RADIUS attributes to Mist labels.
- Using RADIUS attribute labels in WxLAN policies.
- SMAL integration for third-party identity providers in administrator logins.
Juniper Mist AIOps System Training Course Prerequisites
- Basic networking (wired and wireless) knowledge
- Understanding of the Open Systems Interconnection (OSI) reference model and the TCP/IP protocol suite
- Basic scripting knowledge; Python knowledge recommended
- Completion of the Juniper Mist AI Networks course and Introduction to Juniper Mist AI course, or equivalent experience
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