Deep Dives

Constructing a Rules Engine from First Principles

If you could have ever been accountable for managing complex business logic, you understand how nested if-else statements is usually a jungle: painful to navigate and simple to wander off. With regards to mission-critical...

Constructing a Monitoring System That Actually Works

and managing products, it’s crucial to make sure they’re performing as expected and that the whole lot is running easily. We typically depend on metrics to gauge the health of our products. And...

The Power of Framework Dimensions: What Data Scientists Should Know

A previous article provided a of conceptual frameworks – analytical structures for representing abstract concepts and organizing data. Data scientists use such frameworks in a wide range of contexts, from use case ideation and...

Constructing a Geospatial Lakehouse with Open Source and Databricks

Most data that pertains to a measurable process in the true world has a geospatial aspect to it. Organisations that manage assets over a large geographical area, or have a business process which requires...

Why Should We Trouble with Quantum Computing in ML?

When black cats prowl and pumpkins gleam, may luck be yours on Halloween. (Unknown) , conferences, workshops, articles, and books on quantum computing have multiplied, opening recent ways to process information and to reconsider the...

Scaling Recommender Transformers to a Billion Parameters

! My name is Kirill Khrylchenko, and I lead the RecSys R&D team at Yandex. One in all our goals is to develop transformer technologies inside the context of recommender systems, an objective we’ve...

Tips on how to Construct An AI Agent with Function Calling and GPT-5

and Large Language Models (LLMs) Large language models (LLMs) are advanced AI systems built on deep neural network akin to transformers and trained on vast amounts of text to generate human-like language. LLMs like ChatGPT,...

Conceptual Frameworks for Data Science Projects

are analytical structures for representing abstract concepts and organizing data. Data scientists usually use such frameworks — knowingly or unknowingly — to derive project plans, select machine learning models that balance various trade-offs,...

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