📌 Denormalisation: Thoughtful Optimisation or Irrational Avant-Garde?

🗂 Category: DATA SCIENCE

🕒 Date: 2024-08-10 | ⏱️ Read time: 19 min read

Perspective on Performance Optimisation and Data Quality

📌 Which Regression technique should you use?

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2024-08-10 | ⏱️ Read time: 12 min read

Here’s a taxonomy of what is the best regression technique based on your specific dataset

📌 Data Scaling 101: Standardization and Min-Max Scaling Explained

🗂 Category: DATA ENGINEERING

🕒 Date: 2024-08-10 | ⏱️ Read time: 5 min read

When to use MinMaxScaler vs StandardScaler vs something else

📌 Algorithm-Agnostic Model Building with MLflow

🗂 Category: MACHINE LEARNING

🕒 Date: 2024-08-10 | ⏱️ Read time: 10 min read

A beginner-friendly step-by-step guide to creating generic ML pipelines using mlflow.pyfunc

📌 Running a SOTA 7B Parameter Embedding Model on a Single GPU

🗂 Category:

🕒 Date: 2024-08-09 | ⏱️ Read time: 19 min read

In this post I will explain how to run a state-of-the-art 7B parameter LLM based…

📌 Improving Code Quality During Data Transformation with Polars

🗂 Category:

🕒 Date: 2024-08-09 | ⏱️ Read time: 6 min read

Optimize your data workflows with Polars by improving code quality and refining transformations with these…

📌 Structured Outputs and How to Use Them

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2024-08-09 | ⏱️ Read time: 5 min read

Building robustness and determinism in LLM applications

📌 Pre-Commit & Git Hooks: Automate High Code Quality

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2024-08-09 | ⏱️ Read time: 6 min read

How to improve your code quality with pre-commit and git hooks

📌 KernelSHAP can be misleading with correlated predictors

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2024-08-09 | ⏱️ Read time: 7 min read

A concrete case study

📌 AI for the Absolute Novice – Intuitively and Exhaustively Explained

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2024-08-09 | ⏱️ Read time: 40 min read

From “I’ve never coded” to making an AI model from scratch.

📌 LLMOps – Serve a Llama-3 model with BentoML

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2024-08-09 | ⏱️ Read time: 5 min read

Quickly set up LLM APIs with BentoML and Runpod

📌 We Need to Raise the Bar for AI Product Managers

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2024-08-09 | ⏱️ Read time: 10 min read

How to Stop Blaming the ‘Model’ and Start Building Successful AI Products

📌 Create Stronger Decision Trees with bootstrapping and genetic algorithms

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2024-08-09 | ⏱️ Read time: 31 min read

A technique to better allow decision trees to be used as interpretable models

📌 Ask Not What AI Can Do for You – Ask What You Can Achieve with AI

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2024-08-08 | ⏱️ Read time: 11 min read

Unlock AI for Everyone: Discover How You Can Use LLMs in Everyday Tasks

📌 3 Key Tweaks That Will Make Your Matplotlib Charts Publication Ready

🗂 Category: DATA SCIENCE

🕒 Date: 2024-08-08 | ⏱️ Read time: 4 min read

Matplotlib charts are an eyesore by default – here’s what to do about it.

📌 The Big Questions Shaping AI Today

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2024-08-08 | ⏱️ Read time: 4 min read

Our weekly selection of must-read Editors’ Picks and original features

📌 5 Proven Query Translation Techniques To Boost Your RAG Performance

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2024-08-08 | ⏱️ Read time: 11 min read

How to get near-perfect LLM performance even with ambiguous user inputs

📌 How to Use Machine Learning to Inform Design Decisions and Make Predictions

🗂 Category: DATA SCIENCE

🕒 Date: 2024-08-08 | ⏱️ Read time: 15 min read

An Introductory Guide and Use Case for Applied Data Science

📌 Spatial Interpolation in Python

🗂 Category: DATA SCIENCE

🕒 Date: 2024-08-08 | ⏱️ Read time: 4 min read

Using the Inverse Distance Weighting method to infer missing spatial data

📌 Reinforcement Learning, Part 6: n-step Bootstrapping

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2024-08-07 | ⏱️ Read time: 7 min read

Pushing the boundaries: generalizing temporal difference algorithms