Artificial Intelligence & Machine Learning
Welcome to my exploration of AI and machine learning—a field where I’ve spent significant professional time as a data scientist and ML engineer. This section captures insights from building production systems, reading extensively on the topic, and developing strategic perspectives on AI implementation.
I explore both the technical foundations and strategic implications, from hands-on model development to AI Strategy for organizational transformation.
Understanding AI: Beyond the Hype
A fundamental challenge in AI discourse is anthropomorphism—our tendency to attribute human-like qualities to AI systems. We imagine AI as colleagues, threats, or partners rather than sophisticated tools that transform inputs into outputs with statistical patterns.
This perspective matters because it shapes policy, investment, and implementation decisions. AI systems are:
- Probabilistic tools that process information based on learned patterns
- Domain-specific rather than generally intelligent
- Amplifiers of human capability rather than replacements
The regulatory risk isn’t AI becoming “too powerful”—it’s regulators destroying useful tools while chasing dragons that are actually windmills. Like Don Quixote’s misguided heroism, well-intentioned but misguided AI regulation could eliminate tools that significantly improve human productivity and capability.
Essential Reading on AI Foundations
- Designing Machine Learning Systems - Systematic approach to ML system design. Perhaps the msot important book i read on AI.
- Machine Learning Engineering - Production ML best practices
- Interpretable Machine Learning - Making AI systems explainable
Other books that can be of interest is:
Data Science & Analytics
Data science forms the foundation of effective AI implementation. My experience spans the full data science lifecycle, from initial problem framing to model deployment and monitoring.
Pages:
- Data Science Project Start-Up Phase - Lessons learned from real-world project initiation
- Confident Learning - Techniques for handling noisy labels and improving data quality
- Feature Engineering - Transforming raw data into model-ready features
- Evaluation - Comprehensive approaches to assessing model performance
Recommended Books:
- Data Science the Hard Parts - Real-world challenges beyond algorithms
- Analytical Skills for AI and Data Science - Critical thinking for data professionals
- Fighting Churn With Data - Applied ML for business problems
- Analytical Skills for AI and Data Science Also an interesting book. Learned a lot from this.
Other books such as Spark Modelling Mindsets Getting Started with Streamlit for Data Science Turning Data Into Wisdom are intersting.
🗣️ Natural Language Processing & LLMs
The LLM revolution has transformed how we process and generate text. My exploration covers both practical applications and theoretical understanding of language models.
Core Topics:
- Large Language Models - Architecture, training, and deployment strategies
- Fine-tuning - Adapting pre-trained models for specific tasks
- NLP Fundamentals - Classical and modern approaches to language processing
Note: My 2023 LLM writings capture the rapid evolution of this field—some details may be outdated, but the fundamental principles remain valuable.
🏗️ ML Engineering & Operations
Production ML Systems:
- Machine Learning Design Patterns - Reusable solutions for ML systems
- Practical MLOps - Operational best practices
- MLflow Engineering - Experiment tracking and model management
Specialized Applications:
- Recommendation Systems - Building effective recommendation engines
- Pricing Optimization - ML for revenue management
Links
Learning & Development:
- The conspiracy to make AI harder than it is! - Demystifying AI complexity
- ML Engineering Reading List - Current research and trends
- MLAbonne’s Blog - Practical ML guides and tutorials