The Machine Learning Engineer’s Checklist: Best Practices for Reliable Models
Building newly trained machine learning models that work is a relatively straightforward endeavor, thanks to mature frameworks and accessible computing power.
How LLMs Choose Their Words: A Practical Walk-Through of Logits, Softmax and Sampling
This article is divided into four parts; they are: • How Logits Become Probabilities • Temperature • Top- k Sampling • Top- p Sampling When you ask an LLM a question, it outputs a vector of logits.
3 Feature Engineering Techniques for Unstructured Text Data
Machine learning models possess a fundamental limitation that often frustrates newcomers to natural language processing (NLP): they cannot read.
3 Subtle Ways Data Leakage Can Ruin Your Models (and How to Prevent It)
Data leakage is an often accidental problem that may happen in machine learning modeling.
Creating a Llama or GPT Model for Next-Token Prediction
This article is divided into three parts; they are: • Understanding the Architecture of Llama or GPT Model • Creating a Llama or GPT Model for Pretraining • Variations in the Architecture The architecture of a Llama or GPT model is simply a stack of transformer blocks.
Top 5 Agentic AI LLM Models
In 2025, “using AI” no longer just means chatting with a model, and you’ve probably already noticed that shift yourself.
Training a Tokenizer for Llama Model
Let’s get started.
Prompt Engineering for Time Series Analysis
Strange as it may sound, large language models (LLMs) can be leveraged for data analysis tasks, including specific scenarios such as time series analysis.
The Complete Guide to Using Pydantic for Validating LLM Outputs
Large language models generate text, not structured data.
The Roadmap for Mastering Agentic AI in 2026
Agentic AI is changing how we interact with machines.