Contents# Introduction# 1. Qwen3.6 27B MTP# 2. Gemma 4 31B IT QAT# 3. DiffusionGemma 26B A4B# 4. Nemotron Cascade 2 30B A3B# 5. Qwen3.5 9B MTP# 6. EXAONE 4.5 33B# 7. North Mini Code 1.0# Final Thoughts # 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 …
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 …
Contents# Introduction# Setting Up the Dataset# 1. Measuring Time Between Events with LAG()# 2. Comparing a Row to Other Rows in the Same Table with a Self-Join# 3. Selecting the Top Row per Group with ROW_NUMBER()# 4. Segmenting Customers by Spend with NTILE(n)# 5. Smoothing Noisy Data with a Rolling Window# 6. Aggregating Conditionally with FILTER# 7. Detecting Consecutive Activity Streaks with Window …
Practical SQL Tricks Every Data Scientist Should Know Read More »
ContentsIntroductionBarrier 1 | Context and failure recallBarrier 2 | Elastic infrastructureBarrier 3 | Operational Agents and Quality DataBarrier 4 | Git for DataBarrier 5 | Pervasion through the industryBarrier 6 | Agent Sandboxes and New OrchestratorsBarrier 7 | Standards for Proxy Servers and Agent DefinitionPutting it all together | A Single Pane of Glass for AI Introduction , AI examples …
7 Crucial Barriers Between Data Teams and Self-Healing Data Architecture Read More »
Contents# Introduction# What Is a Loss Function?# Mean Squared Error# Mean Absolute Error# Cross-Entropy Loss# Loss vs. Accuracy# The Training Loop# Final Thoughts # 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” …
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 …
Contents# Introduction# Step 1: Building the Foundation# Step 2: Designing Prompts and Building Tool-Calling Systems# Step 3: Building Retrieval Systems Beyond the Basics# Step 4: Fine-Tuning and Aligning Models# Step 5: Serving and Operating LLM Applications# Recommended Learning Resources# Final Thoughts # Introduction An LLM engineer is not the same thing as a general machine learning engineer. Where a machine learning engineer might …
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 …
