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Global AI Regulations: Navigating the Next Frontier

In November 2022, the launch of ChatGPT marked a groundbreaking milestone in AI, highlighting the need for the evolution of global AI regulations. In fact, demand for this evolving technology is so substantial that Gartner estimates more than 80% of enterprise companies will have utilized Generative AI APIs or deployed Generative AI-enabled applications by 2026.  […]

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Revolutionizing Enterprise Solutions With Generative AI

Over the last two years, Generative AI has proven to be more than just a fad. As enterprise organizations move from simple, initial experimentation to extensive Large Language Model (LLM) rollouts, the use cases for adoption have created new strategic opportunities from optimizing internal machine learning deployment lifecycles. Let’s delve into the various LLM use

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The Future of Seldon: Strengthening our Commitment to Open Core

Since Seldon was founded in 2014, its mission has been to accelerate the adoption of machine learning. Thanks to unwavering support and guidance from our customers, community members and investors, this vision has turned into a reality, with over 10 million machine learning models deployed across the world’s most innovative companies.   The vast majority of

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Seldon Named in the 2023 Gartner® Market Report, A CTO’s Guide to the Generative AI Technology Landscape

The 2023 Gartner report, A CTO’s Guide to the Generative AI Technology Landscape, recognizes Seldon Technologies as a Sample Vendor.  Seldon is listed as a Sample Vendor in the GenAI market. What is Generative AI? According to Gartner, “Generative AI (GenAI) learns from previous existing training data like images, videos, or text to generate new

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Deploying Large Language Models in Production: Orchestrating LLMs

In our earlier blog posts in this series, you’ve explored an overview of LLMs and a deep dive into the challenges in deploying individual LLMs to production. This involves striking a balance between cost, efficiency, latency, and throughput–all key elements for achieving success with AI.  In this blog post, we will discuss some of the

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Deploying Large Language Models in Production: LLM Deployment Challenges

In part 1 of this series, we discussed the rise of Large Language Models (LLMs) such as GPT-4 from OpenAI and the challenges associated with building applications powered by LLMs and LLM deployment. Today, we will focus on the deployment challenges that come up when users want to accomplish LLM deployment within their own environment.

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Deploying Large Language Models in Production: The Anatomy of LLM Applications

Large Language Models (LLMs) like GPT-4 and Llama 2 have opened up new possibilities for conversational AI. Companies worldwide are eager to build real-world applications leveraging these powerful models. However, taking LLMs into production introduces a unique set of challenges based on the size and complexity of these models. In this comprehensive blog series, we’ll

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Exploring LLM Apps: the LangChain Paradigm and Future Alternatives

Large language models are performing increasingly impressive tasks, but practitioners are still actively researching how to optimize the functionality of LLMs. Today we’ll be exploring agentic LLMs  and the current LLM LangChain paradigm. “Agentic AI” might be the next natural macro AI trend after “generative ai”.  Read on to discover where the current LangChain paradigm

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