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  • How to structure machine learning projects using GitHub and VS Code: complete instructions with settings and templates

    How to structure machine learning projects using GitHub and VS Code: complete instructions with settings and templates

    A well-designed process for structuring machine learning projects can help you create new GitHub repositories quickly and navigate an elegant software architecture from the start. The VS Cloud team has translated an article on how to organize files in machine learning projects using VS Code. A template for creating machine learning projects can be downloaded on GitHub.

    Note

    To create a new machine learning project from the GitHub template, go to the GitHub repository and click “Use this template”. GitHub template repositories are a very handy thing: they allow me and other users to generate new repositories with the same structure, branches, and files as the template.

    The next page opens up project settings, such as repository name and privacy settings:

    Having created the repository, click “Actions” on the top menu and wait a bit:

    If a green checkmark appears, the project is ready – you can write code!
    Next I’ll tell you why a particular file is added to the project and how the GitHub template was created.

    Basic files

    First, let’s look at the main files of the project, created on the basis of the template:

    .gitignore

    From the .gitignore file, GitHub draws information about which files to ignore when you commit a project to the GitHub repository. If you are creating a new repository from scratch, you can specify a pre-configured .gitignore file.

  • Design patterns for machine learning systems.

    Design patterns for machine learning systems.

    The main goal of this article is to list and describe system patterns for designing machine learning systems in a production environment. Design patterns that help in the development of machine learning models that achieve certain accuracy metrics are not a priority, although for some of the patterns listed, such usecases will still be specified.

    Minimum requirements
    All of the machine learning system patterns listed here are intended for deployment in a public cloud environment or on a Kubernetes cluster. For the purpose of this review, we have tried to abstract as much as possible from specific programming languages or platforms, although since Python is the most significant language for machine learning technology, most of the patterns can be implemented using Python.

    Patterns
    Service systems design patterns
    Service systems design patterns are a set of off-the-shelf design solutions that can be used to organize production workflows that involve machine learning models.

    Web single pattern
    Using
    When you need to quickly release a predictor in the simplest architecture.

    The architecture
    The Web single pattern offers an architecture where all the artifacts and code for the predictor model are enclosed in a single web server. Because in this pattern the REST (or GRPC) interface, preprocessing, and trained model are all in one place (on the server), you can create and deploy them as a simple predictor.
    If you need to deploy multiple replicas at once, you will have to implement a load balancer or proxy server to access them. If you use GRPC as your interface, you should seriously consider implementing a client-side load balancer or layer-7 load balancer.

    To build your model into a web server, you can use the model-in-image pattern or the model-loading pattern.

    Diagram

    Pros of
    Ability to use a single programming language, such as Python, for web server, preprocessing and logical output.

    • Easy to manage because of its simplicity.
    • It is easier to troubleshoot.
    • Minimally time-consuming to refine the model.
    • As a starting production architecture, it is usually recommended to deploy on a single web server with synchronous processing.

    Disadvantages

    • Since all components are encapsulated in a single server or docker image, even a small patch would require updating the entire image.
    • Upgrades will also require a service deployment, which in larger organizations requires doing a serious SDLC.

    What you need to think about

    • Procedures for upgrades and maintenance for each component.
    • Scaling involves changes to web server management.