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.

LLM Prompt Best Practices For Large Context Windows

Published
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.

Read more

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

Published
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.

Read more

Introduction to the EU AI Act

Published
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.

Read more

Calculating LLM Token Counts: A Practical Guide

Published
Author
Natalia Kuzminykh
Associate Data Science Content Editor

This article discusses the concept of token counts in large language models (LLMs) and their impact. Tokens are fragments of language used for text processing, representing words, parts of words, or punctuation marks. Code walkthroughs demonstrate how to calculate token counts and examples provide insight.

Read more

The Problem of Big Data in Small Context Windows (Part 2)

Published
Author
Dr. Phil Winder
CEO

An introduction to the challenge of fitting big data into the context windows of LLMs. In this second installment, discover the key strategies involved to improve your use of the context window. Subsequent articles will provide more examples.

Read more

ChatGPT from Scratch: How to Train an Enterprise AI Assistant

Published
Author
Dr. Phil Winder
CEO

This is a video of a presentation investigating how large language models are built and how to use them, inspired by our large language model consulting work. First presented at GOTO Copenhagen in 2023, the video investigates the history, the technology, and the use of large language models. The demo at the end is borderline cringe, but it’s a fun and demonstrates how you would fine-tune a language model on your proprietary data.

Read more

Part 6: Useful ChatGPT Libraries: Productization and Hardening

Published
Author
Natalia Kuzminykh
Associate Data Science Content Editor

LangChain and LlamaIndex streamline ChatGPT and LLM application development. Boost your project’s efficiency with LangChain’s tools and modules, and LlamaIndex’s advanced document handling. Discover the future of language model orchestration today.

Read more

MLOps in Supply Chain Management

Published
Author
Dr. Phil Winder
CEO

Interos, a leading supply chain management company, partnered with Winder.AI to enhance their machine learning operations (MLOps). Together, we developed advanced MLOps technologies, including a scalable annotation system, a model deployment suite, AI templates, and a monitoring suite. This collaboration, facilitated by open-source software and Kubernetes deployments, significantly improved Interos’ AI maturity and operational efficiency.

Read more

Part 5: How to Monitor a Large Language Model

Published
Author
Natalia Kuzminykh
Associate Data Science Content Editor

The article explores the complexities and nuances of monitoring and evaluating Large Language Models (LLMs) like ChatGPT in business applications. It emphasizes the insufficiency of traditional metrics, the importance of real-time tracking, human feedback, and specialized evaluation methods to ensure model safety, efficiency, and performance optimization.

Read more
}