Mlflow

Complete MLOps Series: Part 1 (current) | Part 2: Deployment → | Part 3: Production → Anatomy of an MLOps Pipeline - Part 1: Pipeline and Orchestration Why This Post Is Not Another Scikit-Learn Tutorial Most MLOps posts teach you how to train a Random Forest in a notebook and tell you “now put it in production.” This post assumes you already know how to train models. What you probably don’t know is how to build a system where: ...

Complete MLOps Series: ← Part 1: Pipeline | Part 2 (current) | Part 3: Production → Anatomy of an MLOps Pipeline - Part 2: Deployment and Infrastructure 8. CI/CD with GitHub Actions: The Philosophy of Automated MLOps The Philosophical Foundation: Why Automation Isn’t Optional Before diving into YAML, let’s address the fundamental question: why do we automate ML pipelines? The naive answer is “to save time.” The real answer is more profound: because human memory is unreliable, manual processes don’t scale, and production systems demand reproducibility. ...

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: ...