Peter Song 
Retrieval-Augmented Generation (RAG) systems have become the go-to architecture for building LLM applications that need to reference specific knowledge bases, documents, or proprietary data. Unlike standalone language models that rely solely on their training data, RAG systems retrieve relevant information from external sources before generating responses. This added complexity means evaluation requires assessing not just … Read more
The transformer architecture revolutionized the field of deep learning when it was introduced in the seminal 2017 paper “Attention Is All You Need.” Understanding the layer architecture of transformers is essential for anyone working with modern natural language processing, computer vision, or any domain where these models have become dominant. At its core, the transformer’s … Read more
Choosing the right large language model for your application is one of the most consequential decisions in AI development. With dozens of models available—from GPT-4 and Claude to open-source alternatives like Llama and Mistral—each claiming superior performance, how do you cut through the marketing and make an evidence-based choice? The answer lies in systematic comparison … Read more
As large language models become increasingly central to production applications, developers are discovering that simple, single-prompt interactions often fall short of solving complex problems. Two sophisticated techniques have emerged to address these limitations: prompt tokening and prompt chaining. While both approaches aim to enhance LLM capabilities and outputs, they operate on fundamentally different principles and … Read more
In the world of machine learning operations, feature pipelines serve as the critical infrastructure that transforms raw data into the features your models consume. The architecture you choose—batch or streaming—fundamentally shapes your system’s capabilities, performance characteristics, and operational complexity. Understanding the nuances between these two approaches is essential for building ML systems that meet your … Read more
Training transformer models effectively requires navigating numerous technical challenges that can derail even well-planned projects. From gradient instabilities to memory constraints, these pitfalls can lead to poor model performance, wasted computational resources, and frustrating debugging sessions. Understanding these common issues and implementing proven solutions is crucial for successful transformer training. The Learning Rate Trap: Finding … Read more
Gradient descent stands as the backbone of modern machine learning optimization, powering everything from simple linear regression to complex neural networks. While the basic concept remains consistent across variants, understanding the nuances between different gradient descent algorithms can dramatically impact your model’s performance, training speed, and convergence behavior. This comprehensive guide explores the most important … Read more
Reinforcement learning has revolutionized artificial intelligence by enabling machines to learn optimal decision-making through interaction with their environment. At the heart of this paradigm lies the Markov Decision Process (MDP), a mathematical framework that provides the foundation for understanding and solving sequential decision problems. In this comprehensive guide, we’ll explore practical Markov Decision Process examples … Read more
Machine learning models only create business value when they’re deployed reliably, monitored continuously, and updated seamlessly. MLOps—the practice of operationalizing machine learning—bridges the gap between data science experiments and production systems. This tutorial walks through building a complete MLOps pipeline using Kubernetes for orchestration and scalability, and MLflow for experiment tracking, model registry, and deployment. … Read more
Amazon Managed Workflows for Apache Airflow (MWAA) removes the operational burden of running Airflow while giving you the full power of this industry-standard workflow orchestration platform. Scheduling jobs effectively in MWAA requires understanding not just Airflow’s scheduling capabilities, but also how to leverage AWS services, optimize for the managed environment, and design DAGs that scale … Read more
