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Developments in AI are changing fast, RAG and CAG are the latest developments that need to be understood. Each has their benefits and differ in where they can be applied.

LLMs are being enhanced by RAG and AI Agents, which are improving accuracy and limiting hallucinations.

After initial overfitting on small datasets, models can eventually achieve exceptional generalisation through extended training and optimisation.

This blog post provides practical tips and tricks for data cleaning, covering methods to handle missing values, standardize formats, remove duplicates, deal with outliers, clean text, and automate the cleaning process. It emphasizes the importance of proper data cleaning as a foundation for meaningful data analysis.

This blog post provides a comprehensive guide on leveraging JupyterLab to enhance data science workflows, emphasizing best practices for notebook management, advanced features for interactivity and customization, and integration with various tools and services

Maintaining a good in-production model requires understanding the understanding of model and concept drift.

The typical data science E2E workflow and pipeline.

Python objects can be sneaky and we need to be careful when passing mutable data types as parameters.

Sometimes you need a concise place to store descriptions about AWS offerings you would use.

SHAP and Synergy matrix will be an important part of a machine learning pipeline.