Applied AI for IT Operations (AIOps)

Introduction

• Artificial intelligence and its many uses
• What you should know

1. IT Operations and AI

• Introduction to ITOps
• ITOps challenges
• AI and ITOps
• ITOps use cases overview
• Setting up the exercise files

2. Use Case 1: Root Cause Analysis

• What is root cause analysis?
• Classification with deep learning
• Data for root cause analysis (RCA)
• Preprocessing RCA data
• Building a classification model with Keras
• Predicting root causes with Keras

3. Use Case 2: Self-Help Service Desk

• Automating helpdesk functions
• Latent semantic analysis (LSA) and latent semantic indexing (LSI)
• Data for the help desk
• Building a document vector
• Creating the LSI model
• Recommending FAQs

4. Use Case 3: Service Load Forecasting

• Time series forecasting
• Recurrent neural network (RNN) and long short-term memory (LSTM)
• Preparing sequence data
• Building an LSTM model with Keras
• Testing the time series model
• Forecasting future service loads with Keras

5. Other ITOps Use Cases

• Anomaly detection
• Predicting alerting
• Incident categorization
• Spam filtering
• Network traffic analysis

6. Generative AI for ITOps

• Generative AI review
• Text generation with LLMs
• Log data extraction
• Incident summarization
• Documentation self-help chatbot
• Code generation for scripts
• Code generation example

7. ITOps Best Practices

• Model development best practices
• Using machine learning platforms
• Model serving best practices
• Security and privacy best practices

Conclusion

• Next steps