AI and Machine Learning

Machine learning systems, model pipelines, embeddings, vector search, and the operational tradeoffs behind training, serving, evaluation, and drift.

ChatGPT Architecture and Training

ChatGPT through tokenisation, transformer prediction, and post-training controls.

Data pipeline phases from collection and storage to transformation and use.

AI agents pair a model with goals, tools, memory, and feedback loops.

Open-source AI stack components for models, retrieval, orchestration, and serving.

DeepSeek models through cost, reasoning behaviour, and deployment trade-offs.

ChatGPT timeline from early language models to transformers and human feedback.

Data platform terms across warehouses, lakes, marts, pipelines, and lakehouses.

Five Pandas merge functions for joins, alignment, and table combination.