The Evolving Landscape of Large Language Model (LLM) Architectures
The field of natural language processing (NLP) has witnessed tremendous progress in recent years, driven by the emergence of large language models (LLMs). These models have revolutionized the way we approach NLP tasks, from language translation and text summarization to question answering and text generation. In this blog post, we’ll explore the evolving landscape of LLM architectures, their history and current trends.
read moreUnderstanding Large Language Models (LLMs): Key Concepts and Terminologies
Large Language Models (LLMs) have changed the field of natural language processing (NLP
), powering applications from chatbots and virtual assistants to machine translation and content generation. Models like OpenAI’s GPT-4, Anthropic’s Claude 3.5 Sonnet or Google’ Gemini 1.5 have demonstrated remarkable results in generating human-like text, performing complex reasoning, and even passing professional exams. But what exactly are LLMs, and what are the key concepts that underpin their functionality?
read moreIn today’s rapidly evolving technological landscape, the shift from building cloud-native platforms to embracing platform engineering has become more critical than ever. As organizations continue to adopt cloud-native architectures and strive for platform maturity, understanding how to build, maintain, and evolve these systems over time is essential. This blog post explores key principles that underpin platform engineering in the context of building and evolving cloud-native platforms, emphasizing how maturity becomes increasingly important over time, and offering insights into how you can keep your platforms effective throughout their required lifespan.
read moreIn recent years, Platform Engineering has emerged as a cornerstone for building and maintaining the infrastructure that powers software development. It focuses on developer experience and ensuring that the paltform is efficient, scalable, and resilient. As artificial intelligence (AI) continues to revolutionise the tech landscape, a new frontier has emerged: AI Engineering. This discipline not only brings AI to the organisation but also adapts the principles of Platform Engineering to meet the unique demands of AI systems. Essentially, AI Engineering is leveraging the proven strategies of Platform Engineering and tailoring them to the complexities of AI technologies.
read moreThe technological landscape is ever-evolving, presenting organisations with both unprecedented opportunities and complex challenges. From the rise of the internet to the shifts toward cloud computing, mobile technologies, and DevOps practices, each leap has demanded swift and strategic adaptation. Now, Generative AI (GenAI) emerges as the next transformative force, poised to revolutionise data handling, automation, and user experiences. However, integrating GenAI isn’t merely about adopting a new tool—it’s about reshaping workflows, infrastructure, and organisational culture. This is where the establishment of a dedicated platform team becomes not just valuable but essential.
read moreAs organizations navigate the complex landscape of cloud computing, artificial intelligence (AI), and Retrieval-Augmented Generation (RAG), platform engineering emerges as a critical foundation. By providing a standardized, self-service platform for developers, it empowers teams to focus on innovation while ensuring operational efficiency and scalability.
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