Category
AI without the hype: models, agents and use cases that work in production.
Spark DataFrames are distributed, schema-based tables that the Catalyst engine optimises automatically, while pipelines chain those transformations into a reproducible end-to-end flow. Together they let you process large data volumes efficiently across a cluster, scaling from a laptop to hundreds of nodes without rewriting code.
ChatGPT 4 combines advanced natural language processing with deep learning to deliver conversations that are more natural, coherent, and personalised than earlier chatbots. It understands intent and accumulated context, handles multiple intents in a single turn, and reduces escalations to human agents, though it still requires careful design and human oversight.
Recommendation systems are the invisible engine behind Netflix, Amazon, and Spotify. Collaborative filtering predicts individual preferences by analysing the behaviour of millions of users without examining item content: 80% of what Netflix viewers watch and 35% of Amazon sales come from algorithmic recommendations.
Explainable AI (XAI) is the set of techniques that open the black box of AI models and answer why they made a given decision. Methods such as LIME, SHAP and Grad-CAM activation maps are the most widely used. Its adoption is mandatory in regulated environments: healthcare, justice, and financial services.
Reinforcement learning is the AI technique in which an agent learns to make optimal decisions through trial and error, without labelled data: it acts in an environment, receives a reward or penalty based on the outcome, and adjusts its strategy to maximise long-term cumulative reward.
Deep neural networks are today the foundation of almost every artificial intelligence application: from facial recognition to machine translation. Built on architectures like CNNs, RNNs, and Transformers, deep learning has transformed computer vision, speech recognition, and natural language processing over the last decade.
