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.

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

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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

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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)

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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

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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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LLM Prompt Best Practices For Large Context Windows

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Author
Natalia Kuzminykh
Associate Data Science Content Editor

This article delves into the nuances of using large language models (LLMs) with large context windows, highlighting the benefits and challenges they present, from enhancing coherence and relevance to demanding more computational resources. Learn practical strategies for prompt design, maintaining narrative coherence, and utilizing attention mechanisms effectively.

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Interview: How The EU AI Act Was Born With Javier Campos

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Author
Dr. Phil Winder
CEO

In this Webinar our CEO Phil Winder sat down with Javier Campos to discuss the EU AI Act. He is chief innovation officer at Fenestra, and is the author of “Grow Your Business with AI: A First Principles Approach for Scaling Artificial Intelligence in the Enterprise”, published by Apress. Javier was involved in the development of the EU AI act, and was also involved in the development of the EU Cookie Law in the early 2010s.

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Introduction to the EU AI Act

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Author
Dr. Phil Winder
CEO

This is a video of a presentation introducing the EU AI Act by explaining what it is, how it impacts you, and what you need to do. In subsequent webinars I will delve into the details and provide specific examples. We will also be speaking to other industry experts to provide their insight.

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