AI in Action with Go
AI in Action with Go
Develop the Real-World Log Analysis Platform and Master Applied AI Techniques
About the Book
Unlock the power of Artificial Intelligence in your Go applications and transform a flood of log data into actionable, real-world insights!
"AI in Action with Go" is your practical, hands-on guide to mastering applied AI techniques by building a sophisticated, open-source log analysis platform called "LogIntelAI" from the ground up. If you're a Go developer looking to move beyond theory and see how AI truly works in practice, this book is your essential companion.
What You Will Build and Learn:
This isn't just a book about concepts; it's about creation. You will:
- Develop "LogIntelAI": Construct a complete, real-world log analysis platform in Go, designed for intelligent data processing and efficient dispatch of findings.
- Master Log Management with Go: Implement robust log ingestion from diverse sources (like files, Elasticsearch, and Kafka), learn advanced preprocessing techniques, and manage data flow with efficient queuing.
- Integrate Cutting-Edge AI: Seamlessly connect your Go applications to powerful AI models (including Large Language Models from services like OpenAI and AWS Bedrock).
- Dispatch Actionable Insights: Learn to format AI-generated insights into structured payloads and send them directly to incident management tools, such as "Versus Incident," turning your analysis into immediate operational value.
- Apply AI Pragmatically: Understand core AI concepts (prompts, tokens, inference) through practical application, without getting bogged down in complex mathematics or abstract theory.
Who This Book Is For:
"AI in Action with Go" is written for intermediate Go developers who are new to Artificial Intelligence but are eager to apply it to solve practical, real-world problems. Whether you're a backend developer, DevOps engineer, SRE, or simply a Go enthusiast curious about AI, this book will provide you with the skills and confidence to:
- Build AI-enhanced Go applications.
- Understand and implement intelligent data processing pipelines.
- Contribute to the exciting field of AIOps (AI for IT Operations).
By the end of this book, you will have not only developed a significant open-source log analysis tool but also mastered applied AI techniques, empowering you to design and build more intelligent, insightful, and impactful Go systems.
Step into the future of software development. It's time to put AI in action with Go!
About the Author
Table of Contents
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Part 1: Foundations - Go for Intelligent Data Processing
- 1. Architecting LogIntelAI for Analysis and Dispatch
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2. Go Essentials for High-Performance Log Processing
- 2.1 Real-World Data Handling: JSON, Text Streams, and Log Formats in Go
- 2.2 Concurrent Operations: Managing Log Sources and AI API Calls Efficiently
- 2.3 Configuration Strategies for a Real-World Go Application
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3. Applied AI Demystified: Concepts for Go Developers
- 3.1 AI, ML, LLMs: A Pragmatic Overview for System Builders
- 3.2 Focus: AI for Log Summarization, Classification, and Anomaly Detection
- 3.3 Interacting with AI Models as Services: The API-First Approach
- 3.4 Key AI Terminology for Integration (Prompts, Tokens, Inference)
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Part 2: Building the LogIntelAI Core: The Go Data Pipeline
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4. Ingesting the Data Flood: Go Log Source Connectors
- 4.1 Designing a Flexible Log Source Connector Interface in Go
- 4.2 In Action: Building a File Log Connector
- 4.3 In Action: Implementing an Elasticsearch Connector
- 4.4 Strategies for Parsing Diverse Log Formats
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5. Refining Raw Data: Log Preprocessing and Normalization in Go
- 5.1 Defining a Standardized Log Event for AI Analysis
- 5.2 In Action: Practical Log Parsing and Data Extraction with Go
- 5.3 Timestamp Management and Data Enrichment Techniques
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6. Managing the Flow: Queuing Logs for AI Analysis
- 6.1 The Role of Buffers and Queues in Data Pipelines
- 6.2 In Action: Implementing an Efficient In-Memory Queue in Go
- 6.3 (Optional) Exploring Persistent Queues (e.g., NATS, Redis) with Go
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7. Formatting for Action: Sending Basic Findings to Versus Incident
- 7.1 Understanding the Versus Incident API (or a generic webhook target)
- 7.2 Designing the "Dispatcher" Interface in Go
- 7.3 In Action: Implementing a Go Dispatcher for Versus Incident
- 7.4 Sending Simple, Rule-Based Findings (e.g., keyword matches)
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4. Ingesting the Data Flood: Go Log Source Connectors
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Part 3: Infusing Intelligence: Go and AI Model Integration
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8. Go Meets AI: Crafting AI Model Connectors
- 8.1 The AI Model Connector Interface: Go's Bridge to Artificial Intelligence
- 8.2 In Action: Calling OpenAI-Compatible APIs (GPT models) from Go
- 8.3 In Action: Leveraging AWS Bedrock (Claude, Llama) with Go
- 8.4 Building Resilient API Clients: Error Handling and Retries in Go
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9. The AI Orchestrator: Directing Intelligent Analysis
- 9.1 Designing the AI Orchestration Engine for Effective Log Analysis
- 9.2 In Action: Routing Logs to Appropriate AI Models and Prompts
- 9.3 Managing Context and State for Enhanced AI Insights
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10. AI in Action: Dynamic Log Summarization with LLMs
- 10.1 Using LLMs to Distill Complex Log Sequences into Clear Summaries
- 10.2 In Action: Effective Prompt Engineering for Log Summarization
- 10.3 In Action: The Go Workflow for LLM-Powered Log Summarization
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11. AI in Action: Log Classification & Anomaly Spotting
- 11.1 In Action: Applying LLMs for Rapid Log Classification (Error Types, Severity)
- 11.2 A Practical Introduction to Anomaly Detection Concepts
- 11.3 In Action (Conceptual/Basic): Using Simple Techniques or APIs for Anomaly Sighting
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12. Intelligent Dispatch: Sending AI-Enriched Data to Versus Incident
- 12.1 In Action: Enhancing the Versus Incident Payload with AI-Generated Summaries, Classifications, and Anomaly Flags
- 12.2 Strategies for Structuring AI Insights for Optimal Incident Response
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8. Go Meets AI: Crafting AI Model Connectors
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Part 4: Advancing LogIntelAI and Your Applied AI Skills
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13. Scaling for Volume: High-Performance AI Pipelines with Go
- 13.1 In Action: Advanced Go Concurrency Patterns for Scalable Log Analysis
- 13.2 Optimizing AI API Interactions for Throughput and Cost
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14. Extending LogIntelAI: Customizing Analysis and Dispatch
- 14.1 A Practical Guide: Building New Log Source Connectors
- 14.2 A Practical Guide: Integrating Different or Custom AI Model Connectors
- 14.3 (Optional) Adapting the Dispatcher for Other Webhook-Based Tools
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15. The Road Ahead: MLOps, Ethics, and Continuous Learning in Applied AI
- 15.1 Brief Introduction to MLOps Considerations for AI-Driven Systems
- 15.2 Ethical AI: Responsible Use of AI in Operational Monitoring
- 15.3 Your Ongoing Journey: Resources for Continued Growth in Go and Applied AI
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16. Joining the Community: Contributing to Open Source LogIntelAI
- 16.1 The Value of Open Source and How to Get Involved
- 16.2 The LogIntelAI Roadmap: Future Directions and Community Goals
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13. Scaling for Volume: High-Performance AI Pipelines with Go
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Appendices:
- A: Setting up Your Go Development Environment
- B: Obtaining API Keys and Setting up Cloud AI Services (OpenAI, AWS Bedrock)
- C: Glossary of Go & Applied AI Terms
- D: Further Reading and Resources
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