Author Archives: ischedio

MLOps

MLOps (short for Machine Learning Operations) is a set of practices, tools, and processes that aim to automate and streamline the lifecycle of machine learning models, from development to deployment and monitoring — much like DevOps does for software engineering. 🔧 MLOps = ML + DevOps It combines: 🔁 Key Stages of the MLOps Lifecycle:Continue reading “MLOps”

AI Agent Loop

Option One An AI Agent Loop refers to the cyclical process by which an autonomous AI agent perceives its environment, plans actions, executes those actions, and reflects on the results. This loop enables the agent to operate intelligently in dynamic environments by continually adapting its behavior based on feedback and outcomes. It is foundational toContinue reading “AI Agent Loop”

Using RAG with existing LLMs

Retrieval-Augmented Generation (RAG) can be effectively used with existing language models (like OpenAI’s GPT-4, Anthropic’s Claude, Meta’s LLaMA, or open-source models via Hugging Face) without needing to retrain them. The core idea is to supplement the model’s knowledge with external documents retrieved at runtime—enhancing factual accuracy, domain relevance, and recency. ✅ How RAG Works withContinue reading “Using RAG with existing LLMs”

RAG pipeline building frameworks comparison

When building a Retrieval-Augmented Generation (RAG) pipeline, the “best” tool depends on your goals, level of abstraction, and control you want over the components. Here’s a breakdown of LangChain, Hugging Face, and PyTorch, to help you choose: 🧱 1. LangChain: Best for: Rapid prototyping and production-ready apps with modular components ✅ Use LangChain if youContinue reading “RAG pipeline building frameworks comparison”