AI, Machine Learning, Reinforcement Learning, and MLOps Articles

Learn more about AI, machine learning, reinforcement learning, and MLOps with our insight-packed articles. Our AI blog delves into industrial use of AI, the machine learning blog is more technical, the reinforcement learning blog is industrially renowned, and our mlops blog discusses operational ML.

Testing and Evaluating Large Language Models in AI Applications

Published
Author
Dr. Phil Winder
CEO

With the rapidly expanding use of large language models (LLMs) in downstream products, the need to ensure performance and reliability is crucial. But with random outputs and non-deterministic behaviour how do you know if you application performs, or works at all? This webinar offers a comprehensive, vendor-agnostic exploration of techniques and best practices for testing and evaluating LLMs, ensuring they meet the desired success criteria and perform effectively across varied scenarios.

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Retrieval-Augmented Generation (RAG) Examples and Use Cases

Published
Author
Dr. Phil Winder
CEO

Watch our webinar to explore Retrieval Augmented Generation (RAG) and its integration with Large Language Models (LLMs). Learn about RAG use cases, advanced LLM architectures, and techniques to enhance AI applications. Ideal for professionals utilizing or interested in RAG and LLM-powered systems.

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Generating Keywords Automatically With llama-cli and Phi-3

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Author
Dr. Phil Winder
CEO

Automate keyword generation for your Hugo blog with llama-cli and Phi-3. Learn how Dr. Phil Winder used local language models to generate SEO-friendly keywords for Winder.AI, enhancing related content linking and site performance. Get the complete script and code explanation for effortless keyword management.

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Build a Voice-Based Chatbot with OpenAI, Vocode, and ElevenLabs

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Author
Natalia Kuzminykh
Associate Data Science Content Editor

Why might we want to make an LLM talk? The concept of having a human-like conversation with an advanced AI model is an interesting idea that has many practical applications. Voice-based models are transforming how we interact with technology, making interactions more natural and intuitive. By enabling AI to talk, we open the door to numerous practical applications, from accessibility to enhanced human-machine interactions. This guide explores how to create a voice-based chatbot using OpenAI, Vocode and ElevenLabs.

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LLM Architecture: RAG Implementation and Design Patterns

Published
Author
Dr. Phil Winder
CEO

This presentation investigates several common production-ready architectures for RAG and discusses the pros and cons of each. At the end of this talk you will be able to help design RAG augmented LLM architectures that best fit your use case.

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Exploring Small Language Models

Published
Author
Natalia Kuzminykh
Associate Data Science Content Editor

Large language models (LLMs) are powerful but demand significant resources, making them less ideal for smaller setups. Small language models (SLMs) are a practical, resource-efficient alternative, offering quicker deployment and easier maintenance. This article discusses the benefits and applications of SLMs, focusing on their efficiency, speed, robustness, and security in contexts where LLMs are not feasible.

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Big Data in LLMs with Retrieval-Augmented Generation (RAG)

Published
Author
Natalia Kuzminykh
Associate Data Science Content Editor

Retrieval-Augmented Generation (RAG) improves Language Large Models (LLMs) by integrating external data through indexing, retrieval, and generation steps. This method allows LLMs to access up-to-date information and specific details, enhancing their applicability across various domains by providing more accurate, relevant responses and enabling real-time updates and domain-specific customization.

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LLMs: RAG vs. Fine-Tuning

Published
Author
Dr. Phil Winder
CEO

Interest in the use of large language models (LLMs) has ballooned in our recent AI consulting projects because they are applicable to a wide variety of AI problems. But most use cases require the use of proprietary data. What’s the best way of leveraging private or local data in LLMs?

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