Writing Archive

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. ...

Introduction Data scientists often face three possibilities when deploying our products into production: through the cloud, edge computing, or the more recent variant, Edge Machine Learning. To introduce these concepts and provide context for this post, I will start by discussing the foundation—AI Computing. AI Computing and the Rigor of Mathematics A promise of computing is to make machines learn, and this is the job of the data scientist. When applying a model to data, Alan Turing’s insight from 1947 remains highly relevant. He simply stated that “machines learn from experience” (train set), a concept now popularized as data-driven methods. ...

Exploring the intersection of machine learning and DevOps - from model versioning to automated deployments. Featured Posts 📦 Artifact Design and Pipeline in MLOps Part I Introduction to artifacts, MLproject manifests, and pipeline orchestration for reproducible ML workflows. 🤖 MLflow for Generative AI Systems Learn how to use MLflow for tracing, evaluation, and versioning of LLM applications and Agentic AI systems. 🍓 Raspberry Pi 16GB, Servers, and MLOps Using Raspberry Pi as a development server for MLOps testing and edge deployments. ...