Browsing: Engineering

It is 2026, and in the era of Large Language Models (LLMs) surrounding our workflow, prompt engineering is something you must master. Prompt engineering represents the…

Image by Author   # Introduction  As a machine learning practitioner, you know that feature engineering is painstaking, manual work. You need to create interaction terms between…

Prompt engineering isn’t about creating elaborate prompts. It’s about developing the judgment to choose the right structure, logic, and level of control for a given task.…

In this tutorial, we demonstrate how we use Ibis to build a portable, in-database feature engineering pipeline that looks and feels like Pandas but executes entirely…

How far can a mid sized language model go if the real innovation moves from the backbone into the agent scaffold and tool stack? Meta and…

Image by Author   # Introduction  Building Extract, Transform, Load (ETL) pipelines is one of the many responsibilities of a data engineer. While you can build ETL…

Context Engineering Explained in 3 Levels of Difficulty | Image by Author   # Introduction  Large language model (LLM) applications hit context window limits constantly. The model…

Image by Editor   # Introduction  Data engineering is quietly undergoing one of its most consequential shifts in a decade. The familiar problems of scale, reliability, and…

Using a large language model for the first time often feels like you are holding raw intelligence in your hands. They tend to write, summarize, and…

!pip install -q pyspark==3.5.1 from pyspark.sql import SparkSession, functions as F, Window from pyspark.sql.types import IntegerType, StringType, StructType, StructField, FloatType from pyspark.ml.feature import StringIndexer, VectorAssembler from…