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 […]
Revolutionizing Enterprise Solutions With Generative AI Read More »
Seldon\’s LLM Module, an Innovative solution for deploying and managing Large Language Models (LLMs) in Generative AI (GenAI) applications.
Introducing Seldon’s LLM Module: The Next Era in Generative AI (GenAI) Deployment Read More »
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
The Future of Seldon: Strengthening our Commitment to Open Core Read More »
Generative AI has quickly become known outside of the IT landscape in the last year. But what exactly is the difference between generative AI and machine learning? By clearly understanding the differences between the two terms, you can learn how they can add value to your organization in distinct ways. What is Machine Learning? Machine
Generative AI vs. Machine Learning Read More »
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
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
Deploying Large Language Models in Production: Orchestrating LLMs Read More »
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.
Deploying Large Language Models in Production: LLM Deployment Challenges Read More »
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
Deploying Large Language Models in Production: The Anatomy of LLM Applications Read More »
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
Exploring LLM Apps: the LangChain Paradigm and Future Alternatives Read More »
The financial sector is witnessing an alarming surge in fraudulent incidents, creating a critical concern for the industry. In 2021 alone, fraud shot up by 70%, causing losses of over $5.8 billion. This troubling trend, paired with emerging threats like AI-based scams, makes it crucial to use the best tools available. In this post, we’ll
Unlocking the Power of AI in Financial Services: Fraud Prevention with Seldon & Effectiv Read More »
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