
Hands-on guides to the world of MLOps and AIOps.
| Platform | Pricing | Only free issues | Publishes | Weekly | |
|---|---|---|---|---|---|
| Issues | 21 | Founded | a year ago | Last Issue | 5 days ago |
| Active | |||||

In the first tutorial, we explored why MLX is a compelling framework for running local large language models on Apple Silicon. We saw how MLX uses the Mac’s unified memory architecture and how MLX LM adds a practical workflow for working wi...
In the previous tutorials, we built, published, and ran a Docker image containing a FastAPI application. Those steps confirmed that we could distribute the image and run the application, but they didn't show us the metadata and configuratio...
A few years ago, running a large language model (LLM) locally often meant building a home lab with NVIDIA GPUs. I remember my first attempt on my M1-generation Mac with 64 GB of unified memory. A simple prompt such as “Why is the sky blue?”...
In the previous tutorials, we saw how to define the image requirements using a Dockerfile and then build the image using the docker build command. In this tutorial, we will complete our walkthrough of the Docker workflow by covering its f...
In the previous tutorial, How to Build a Docker Image from a Dockerfile, we used a Dockerfile and the docker build command to build the FastAPI image and tag it as rkrispin/docker-for-ai-fastapi:0.1. In many ways, working with images is...
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The writers behind this newsletter.
I write about data science, AI, MLOps, and forecasting.
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