📰 Machine Learning Mastery

This article is divided into four parts; they are: • The Problem with Static Batching • Code Example of Static Batching • Continuous Batching: Dynamic Schedulin

Modern AI agents built on top of large language models (LLMs) are designed to run continuously.

When large language models, or LLMs for short, produce outputs, several criteria are at stake, including not only overall response relevance but also coherence

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Implementing hybrid search strategies is a critical step in building modern RAG (Retrieval-Augmented Generation) systems , especially when shifting from prototy

Keyword search breaks the moment a user types something a document doesn't literally say.

I have been experimenting with the OpenAI Agents SDK, and it has quickly become one of my favorite ways to build agentic AI applications.

Here is the number that defines the current state of things: <a href="https://svitla.

You have probably spent time learning how to prompt AI well.

Search works well when users know exactly what they are looking for, but it breaks down when intent is described in natural language.

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Large language models (LLMs) now power everything from customer service bots to autonomous coding agents.

Agentic loops in production can be synonymous with high costs, especially when it comes to both LLM and external application usage via APIs, where billing is of

AI agents have evolved beyond passive chatbots.

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Non-deterministic agents are those where the same input can lead to distinct outputs across multiple runs.

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TurboQuant has recently been launched by Google as a novel algorithmic suite and library for applying advanced quantization and compression to large language mo

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The idea of building your own AI agent used to feel like something only big tech companies could pull off.

FastAPI has become one of the most popular ways to serve machine learning models because it is lightweight, fast, and easy to use.

A stateless AI agent has no memory of previous calls.