Llms

Post Objective Data cleaning and preliminary analysis process Understanding the emotional charge or plot development of texts through semantic archaeology based on PCAs Understanding the connections and most representative ideas within the document set Intention Understanding a story’s behavior at the level of its variance is a challenge addressed by attentional engineering. Therefore, using lesser-known methods such as the vector narrative arc combined with a literary map constitutes an interesting route to address increasingly common problems. ...

MLflow for Generative AI Systems I’ll start this post by recalling what Hayen said in her book Designing Machine Learning Systems (2022): ‘Systems are meant to learn’. This statement reflects a simple fact: today, LLMs and to a lesser extent vision language models are winning in the Data Science world. But how do we measure this learning? RLHF work is always a good indicator that perplexity will improve, but let’s return to a key point: LLMs must work as a system, therefore debugging is important, and that’s where the necessary tool for every Data Scientist, AI Engineer, ML Engineer, and MLOps Engineer comes in: MLflow. ...

Less than two months ago, the most powerful version of the Raspberry Pi 5 hit the market, featuring 16GB of RAM. While its price ($120 USD) is a valid discussion point, as someone who uses these devices as servers for deployment testing and efficiency evaluation at the code level, I want to explore its utility from a computer science perspective in the context of MLOps and LLMs testing. Raspberry Pi Utility Let’s start with some common applications to build on ideas: ...