When you’re building a production machine learning system, reproducibility is a proxy for the effectiveness of your development process. But without locking all your Python dependencies, your builds are not actually repeatable. If you work in a Python project without locking long enough, you will eventually get a broken build … Read more
If you’re building production ML systems, dev containers are the killer feature of VS Code. Dev containers give you full VS Code functionality inside a Docker container. This lets you unify your dev and production environments if production is a Docker container. But even if you’re not targeting a Docker … Read more
A step-by-step quick start guide for SageMaker Studio. Start a Studio session, launch a notebook on a GPU instance and run object detection inference with a detectron2 pre-trained model.
Building machine learning pipelines as well-formed Python packages simplifies transfer learning. Here’s a simple example.
How you structure code in an ML pipeline makes a big difference in whether other people can easily use it. Here’s a recommendation for how to do it well.
Once you’ve installed Docker, there a few basic features to know. In this post you’ll learn about running containers. If you haven’t gotten started with Docker yet, checkout this quick start guide. The basics You can run Docker containers with a command that takes the following form: The only required element … Read more
Docker is a useful tool for creating small virtual machines called containers. Containers are instances of docker images that are defined in Dockerfiles.