Applied AI for IT Operations

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 desk functions

• Latent semantic analysis (LSA) and latent semantic indexing (LSI)

• Data for the 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