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.

Intro to Vision RAG: Smarter Retrieval for Visual Content in PDFs

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Author
Dr. Phil Winder
CEO

As visual data becomes increasingly central to enterprise content, traditional retrieval-augmented generation (RAG) systems often fall short when faced with richly visual documents like PDFs filled with charts, diagrams, and infographics. Vision RAG is a cutting-edge pipeline that leverages vision models to generate image embeddings, enabling intelligent indexing and retrieval of visual content.

In this session, you’ll explore the state of the art in visual RAG, see a live demo using open-source tools like VLLM and custom Python components, and learn how to integrate this capability into your own GenAI stack. The presentation will also highlight Helix, our secure GenAI platform, showcasing how Vision RAG fits into a scalable, enterprise-ready solution.

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AI in Aviation Case Study: Predicting Taxi Times

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Author
Dr. Phil Winder
CEO

Leveraging predictive analytics and a stand-to-runway modelling approach, Winder.AI and our aviation client improved taxi time predictions to reduced ground delays and improve fuel efficiency.

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Scaling GenAI to Production: Strategies for Enterprise-Grade AI Deployment

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Author
Natalia Kuzminykh
Associate Data Science Content Editor

The article examines the challenges of moving GenAI from prototypes to production. It highlights issues such as resource constraints, performance monitoring, cost management, and security, and suggests strategies for efficient scaling, robust guardrails, and continuous monitoring to ensure sustainable enterprise-grade deployments.

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Enterprise AI Assistants: Combatting Fragmentation

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Author
Dr. Phil Winder
CEO

Enterprise AI Assistants unify disparate data sources, providing real-time insights and access control. Building domain-specific assistants and orchestrating them (hierarchical or federated) offers scalability, specialized features, and better performance than single-vendor solutions. Ultimately, organizations need a tailored approach that consolidates knowledge, fosters collaboration, and addresses evolving AI integration challenges.

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Keynote: Data Transparency, AI Use Cases, Data Sovereignty

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Author
Dr. Phil Winder
CEO

At Winder.AI, we’re seeing a shift in how businesses are adopting AI—not just for innovation, but for real, tangible commercial outcomes. I had the privilege of sharing these insights as a keynote speaker at ITAPA in beautiful Bratislava, Slovakia. The audience was looking for an insight into how AI is being used and some of the key challenges that are being faced today. I took the opportunity to share some of my thoughts about the importance of data transparency, some interesting use cases, and future regulation to watch out for.

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Large Language Model Fine-Tuning via Context Stacking

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Author

Fine-tuning Large Language Models (LLMs) can be a resource-intensive and time-consuming process. Businesses often need large datasets and significant computational power to adapt models to their unique requirements. Attentio, co-founded by Julian and Lukas, is changing this landscape with an innovative technique called context stacking. In this video, we explore how this method works, why it is so efficient, and what it means for enterprises looking to embed custom knowledge directly into their AI models.

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AI in 2024: A Year in Review

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Author
Dr. Phil Winder
CEO

In this reflective podcast, the team at Winder.AI — Dr. Phil Winder, Charles Humble, and Jonathan Hunter — take a deep dive into their year, discussing trends, lessons learned, and their vision for 2025.

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AI Strategy for CEOs: Aligning Tech with Business Goals

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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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