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.

AI Strategy for CEOs: Aligning Tech with Business Goals

Published
Author
Charles Humble
Associate Editor

Join Phil Winder and Charles Humble in this insightful interview as he delves into the critical components of an enterprise AI strategy, based upon his excellent article on the subject.

Gain actionable insights on aligning AI with your business goals, building robust data infrastructure, and prioritizing projects based on ROI. Learn about MLOps, AI security, and the role of generative AI and reinforcement learning across industries.

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Best LLMOps Tools: Comparison of Open-Source LLM Production Frameworks

Published
Author
Natalia Kuzminykh
Associate Data Science Content Editor

Discover how to deploy open-source LLMs using LLM agent frameworks, orchestration frameworks, and LLMOps platforms. Learn about serving frameworks like vLLM and Ollama, and explore LLMOps tools that enhance language model performance in production environments.

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Working with the EU AI Act - Interview with Kai Zenner

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Author
Dr. Phil Winder
CEO

In this webinar I’m pleased to present a recorded interview with Kai Zenner, Head of Office and Digital Policy Adviser for MEP Axel Voss. Kai is a key figure in the EU AI Act, responsible for authoring parts of the regulation, whilst advising and debating the rest.

In this interview we discuss the impact of the EU AI Act on the AI industry, the implications for AI ethics, and the future of AI in Europe. We talk about the potential challenges for AI companies, especially those that lie outside of the EU. We find that although there are holes in the regulation, it is a step in the right direction for the AI industry. Kai makes his suggestions as to how companies can prepare for and help to improve the regulation.

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Artificial Intelligence in the Future of Supply Chains

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Author
Dr. Phil Winder
CEO

The use of artificial intelligence (AI) in supply chains has shifted from being a buzzword to a powerful tool with transformative potential. AI is no longer just an emerging technology—it is actively reshaping how companies approach their supply chain operations. I recently presented how AI, including generative AI, is transforming the supply chain industry. This post dives into the key insights from my presentation, highlighting the role AI plays in improving supply chains and what the future may hold.

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Getting Enterprise AI Strategy Right

Published
Author
Charles Humble
Associate Editor

AI offers considerable benefits across almost every commercial enterprise, from efficiency gains to the potential for brand new products. But it also represents a threat, opening up possibilities for new entrants to disrupt established markets. In business environments, company executives look to produce an ‘AI Strategy’ but can lose sight of their objectives, rather like a politician thinking, “Something needs to be done and this is something, so let’s do this.

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A Comparison of Machine Learning Model Monitoring Tools and Products

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Author
Dr. Phil Winder
CEO

Machine learning (ML) model monitoring is a crucial part of the MLOps lifecycle. It ensures that your models are performing as expected and that they are not degrading over time. There are many tools available to help you monitor your models, from open-source frameworks to proprietary SaaS solutions. In this article, I’ll compare some of the best open-source and proprietary machine learning model monitoring tools available today.

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A Comparison of Open Source LLM Frameworks for Pipelining

Published
Author
Natalia Kuzminykh
Associate Data Science Content Editor

Discover top open source LLM frameworks and orchestration tools. Explore popular LLM projects, including LangChain and LlamaIndex, for seamless integration. Learn about Python LLM libraries, LLM agent frameworks, and the best tools for LLM development and orchestration.

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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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