Patrycja Jenkner    In the machine learning ML and artificial intelligence AI domain, managing, tracking, and visualizing model training processes is a significant challenge due to the scale and complexity of managed data, models, and resources. Union, an optimized and more performant version of the open source...

Aitor Mira Abad    In , when GPT . was introduced with ChatGPT, many, like me, started experimenting with various use cases. A friend asked me if it could read an article, summarize it, and answer some questions, like a research assistant. At that time, ChatGPT had no tools to explore websites, but I was...

Natalia Kuzminykh    Large Language Models LLMs are often unpredictable and hard to control in practice. For example, a model can perform well during testing but fail in production, leading to inconsistent outputs or hallucinations. This unpredictability is intrinsic to the stochastic nature of LLMs: they can produce...

MichaÅ‚ Oleszak    Reinforcement Learning from Human Feedback RLHF has turned out to be the key to unlocking the full potential of today’s large language models LLMs . There is arguably no better evidence for this than OpenAI’s GPT model. It was released back in , but it was only its RLHF trained version...

Ricardo Cardoso Pereira    It is estimated that to of the data worldwide is unstructured. However, when we look for data in a specific domain or organization, we often end up finding structured data. The most likely reason is that structured data is still the de facto standard for quantitative information....

Lucà­a Cordero Sánchez    When I first delved into machine learning, prompt engineering seemed like a niche area, outside of the scope of what an engineer like me needed to know. Yet, as large language models LLMs have evolved, it has become clear that prompt engineering is not only a skill but a critical component in the...

Gourav Bais    Text summarization is a prime use case of LLMs Large Language Models . It aims to condense large amounts of complex information into a shorter, more understandable version, enabling users to review more materials in less time and make more informed decisions. Despite being widely applied in...

Aurimas Griciunas    Observability is invaluable in LLMOps. Whether we’re talking about pretraining or agentic networks, it’s paramount that we understand what’s going on inside our systems to control, optimize, and evolve them. The infrastructure, effort, and scale required to achieve observability vary significantly....

Ejiro Onose    Large Language Models LLMs have become the driving force behind AI powered applications, ranging from translation services to chatbots and RAG systems. Along with these applications, a new tech stack has emerged. Beyond LLMs, it comprises components such as vector databases and orchestration...

Stephen Oladele    As machine learning ML drives innovation across industries, organizations seek ways to improve and optimize their ML workflows. End to end E E MLOps platforms promise to simplify the complicated process of building, deploying, and maintaining ML models in production. However, while E E MLOps...

MichaÅ‚ Oleszak    The growing prevalence of ML models in business critical applications results in an increased incentive for malicious actors to attack the models for their benefit. Developing robust defense strategies becomes paramount as the stakes grow, especially in high risk applications like autonomous...

Gabriel Gonçalves    As a Machine Learning Engineer working with many companies, I repeatedly encounter the same interaction. They tell me how happy they are with ChatGPT and how much general knowledge it has. So, all they want me to do is teach ChatGPT the company’s data, services, and procedures. And then this new...