Posts

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. ...

Reflections on TensorFlow in the context of the modern AI engine and the evolving role of Data Scientists Throughout my journey in the world of data, I’ve witnessed many changes — some tools fading out of popularity while others take the spotlight. R, for example, has become more niche, used mostly by statisticians and academics. Flask, once a common choice for lightweight APIs, gradually gave way to FastAPI thanks to its modularity and support for asynchronous features, redefining how APIs are designed and deployed. ...

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: ...

One of the tools I use most for practicing MLOps, both for designing pipelines and APIs (for inference), is the Raspberry Pi. Today, I spent several hours trying to install Visual Studio Code to complement my iPad Pro as a development tool. Why this setup? 🤔 Improve programming skills—I am a big fan of using Weights & Biases (W&B) to monitor the resource usage of each service I create. Using the Raspberry Pi as a server allows me to test Edge computing deployments. For scalable prototype development, it’s a great way to test artifacts and the lifecycle of models. When designing a model from hyperparameters, it helps me fine-tune grid search or Bayesian methods efficiently to optimize experimentation. Running MLflow on Edge computing enables optimization in model registry and updates. Using Docker and Kubernetes helps ensure clean code before committing changes. There are many more reasons, but these are the main ones. Now, how do you set up Raspberry Pi to unlock its full power for MLOps? ...

Artifact Design and Pipeline in MLOps Part I In MLOps, most of the work focuses on the inference stage, specifically the development of microservices. However, the broader picture goes beyond this—it includes aspects ranging from programming practices to resource utilization that need to be evaluated. This is where the role of a Machine Learning DevOps Engineer becomes crucial. In this post, I want to address this profile by approaching it from the perspective of designing a model. ...