AI Agents and Agentic Systems: From Chat to Action
Chatbots produce text. Agents produce outcomes. The conceptual shift is simple: instead of stopping at an answer, an AI agent […]
AI Agents and Agentic Systems: From Chat to Action Read More »
Chatbots produce text. Agents produce outcomes. The conceptual shift is simple: instead of stopping at an answer, an AI agent […]
AI Agents and Agentic Systems: From Chat to Action Read More »
Imagine you have just built a high-performance race car engine (your Large Language Model). It is powerful, loud, and capable
LLM Deployment: A Strategic Guide from Cloud to Edge Read More »
A deep dive into the art and science of creating artificial data for machine learning. Imagine you’re a master chef
Guide to Synthetic Data Generation: From GANs to Agents Read More »
As machine learning models grow in complexity and size, deploying them on resource-constrained devices like mobile phones, embedded systems, and
ML Model Quantization: Smaller, Faster, Better Read More »
Large ML models often come with substantial computational costs, making them challenging to deploy on resource-constrained devices or in real-time
Pruning of ML Models: An Extensive Overview Read More »
What is DSPy? Declarative Self-improving Python (DSPy) is an open-source python framework [paper, github] developed by researchers at Stanford, designed
DSPy: A New Era In Programming Language Models Read More »