# Introduction Local coding models are finally getting serious. I have been a big fan of this new wave of local large language models (LLMs), especially the open models and community GGML Universal File (GGUF) releases that make them easier to run on consumer hardware. We are now at a point where some of these …
my series on Outlier Detection. In this article, we look at working with categorical data. Generally when performing outlier detection with tabular data, we start by converting the data so that it is either entirely categorical or entirely numeric. There are some exceptions, but for the most part this is necessary: most outlier detection algorithms …
# Introduction Focusing only on SELECT, WHERE, and GROUP BY is enough for basic aggregation, but many real analytical tasks require patterns that go beyond simple queries. Examples include detecting consecutive activity streaks, segmenting customers by spend tier, smoothing noisy time-series data, or tracing plan upgrade paths across rows. This article walks through 7 practical …
Practical SQL Tricks Every Data Scientist Should Know Read More »
Introduction , AI examples of data engineering revolve around one thing: fixing a pipeline. An engineer opens up Claude Code, pastes some logs, and a pull request is made. Semantics are fundamental here. Because when people say “self-healing” what they mean is “self-managing”. The key to success in AI is not defined by manual intervention …
7 Crucial Barriers Between Data Teams and Self-Healing Data Architecture Read More »
# Introduction I know that when beginners start learning machine learning, things seem easy at first. You follow a tutorial that asks you to load a dataset, train a model, and then you see something like this: loss = “mse” or criterion = nn.CrossEntropyLoss(). And just like that, the tutorial starts talking about equations, gradients, …
Loss Function Explained For Noobs (How Models Know They Are Wrong) Read More »
AI has the potential to help solve some of the world’s biggest challenges — not just in the digital realm, but in the physical world, too. Robotics is one of the most exciting frontiers of AI, where advances in language, vision and action models can help create intelligent machines that interact with the real world …
says “this customer’s probability of leaving is 0.4” and your code does predict(X) >= 0.5, you have just made a pricing decision: you decided that the cost of sending a retention offer to a customer who would have stayed is exactly equal to the cost of losing one who would have left, and on the …
# Introduction An LLM engineer is not the same thing as a general machine learning engineer. Where a machine learning engineer might spend months training a neural network from scratch, an LLM engineer’s work centers on adapting, orchestrating, and serving pretrained large language models (LLMs). The job is to take a capable foundation model and …
Today, we are introducing Gemma 4 12B, our latest model designed to bring agentic multimodal intelligence directly to laptops. Bridging the gap between our edge-friendly E4B and our more advanced 26B Mixture of Experts (MoE), Gemma 4 12B packages powerful capabilities inside a reduced memory footprint. It is also our first mid-sized model to feature …
I was asked to do something new at work: Given a data dump of unstructured text data, give us a detailed PDF report of insights about what customers are saying about our products this quarter. So I wrote a clear prompt. Gave Claude a detailed set of instructions. Fed it the dataset. It gave me …
