cookiecutter-data-science
mlops-course
cookiecutter-data-science | mlops-course | |
---|---|---|
17 | 20 | |
7,611 | 2,741 | |
- | - | |
1.6 | 2.1 | |
19 days ago | 9 months ago | |
Python | Jupyter Notebook | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
cookiecutter-data-science
-
Questions about Cookiecutter and Anaconda.
I opened an Anaconda cmd window and ran `cookiecutter https://github.com/drivendata/cookiecutter-data-science ` . I answered all prompted questions. After searching for a while I found where the project folder was created. However, how do I get this on GitHub? The only thing I can figure out is to create a brand new repo on GitHub with the exact same name, open it in GitHub desktop, click "show in explorer", and then drag and drop all files from the Cookiecutter folder into the GitHub Desktop folder. However to me this does not sound like the intended way to create a new project and put it on GitHub.
-
What should the folder structure of my Python projects be?
I'm not sure what "data scraping" means exactly, but for data science generally I think this is a pretty good template: https://github.com/drivendata/cookiecutter-data-science
- What are examples of well-organized data science project that I can see on Github?
-
How to keep a project organized?
Perhaps this cookiecutter template will help: https://github.com/drivendata/cookiecutter-data-science
-
What are some good DS/ML repos where I can learn about structuring a DS/ML project?
I've found https://github.com/drivendata/cookiecutter-data-science as a guide, but haven't found any repos that solve a problem end to end actually use it. Are there any good repos or resources that exemplify how to solve a DS/ML case end-to-end? Including any UI (a report, stream, dash etc) needed for delivery, handling data, preprocessing, training and local development.
- Can anyone share how they structure their folder for data engineer project?
-
Personal Projects that are original
Project don't need to be 100% original, do the project in a such a way that other people has not done yet. There are plenty of datasets and notebooks available on Kaggle. Those are just bunch of notebooks. Take the inspiration from the notebook and build the project in modular structure and organize your project in proper folders and modules. I am using this cookiecutter for building my portfolio projects. https://github.com/drivendata/cookiecutter-data-science
-
How to resolve ModuleNotFoundError error?
Hi all,I am working on ML project and have created project using the cookiecutter-data-science scaffolding. The structure of the project somewhat looks like this,
-
Workflow for early research projects in your organization?
While data science is not SE, it's fundamental to have some structure in your projects since you want the work to be somewhat reproducible. I recommend you start here https://github.com/drivendata/cookiecutter-data-science Since it's a cookie cutter it will be easier to implement at first since they can create the structure by running a short command, after some time you will tailor it to your specific company needs :) For notebooks it's kind of hard, they can't be peer reviewd that easily since cells are editable even after code has been run, keeping the old result... I recommend tools like deepnote, but I'm not sure how well they work for collaboration in notebooks because I never used them yet, I just know they are working on solving these problems. I hope these things help!
-
Github Discussion: What is your favorite Data Science Repo?
Personally I like using cookiecutter’s data science project template. It is easy to set up and has a clear structure. Here is their github: https://github.com/drivendata/cookiecutter-data-science
mlops-course
-
Ask HN: Daily practices for building AI/ML skills?
coming from a similar context, i believe going top down might be the way to go.
up to your motivation, doing basic level courses first (as shared by others) and then tackling your own application of the concepts might be the way to go.
i also observe the need for strong IT skills for implementing end-to-end ml systems. so, you can play to your strenghts and also consider working on MLOps. (online self-paced course - https://github.com/GokuMohandas/mlops-course)
i went back to school to get structured learning. whether you find it directly useful or not, i found it more effective than just motivating myself to self-learn dry theory. down the line, if you want to go all-in, this might be a good option for you too.
- [Q] Any good resources for MLOps?
- Open-Source Machine Learning for Software Engineers Course
-
Open-source MLOps Fundamentals Course 🚀
Find all the lessons here → https://madewithml.com/MLOps course repo → https://github.com/GokuMohandas/mlops-courseMade With ML repo → https://github.com/GokuMohandas/Made-With-ML
-
What are examples of well-organized data science project that I can see on Github?
- https://github.com/GokuMohandas/mlops-course (code for MLOps course)
- Made With ML – develop, deploy and maintain production machine learning
-
Where can I learn more about the engineering part of the role?
Haven’t done it but have heard good reviews - https://github.com/GokuMohandas/mlops-course
-
Path to ML from a backend engineering role
If MLOps, read https://github.com/GokuMohandas/mlops-course 😎
- What skills should I focus on to improve as a MLE?
- MadeWithML – A practical approach to learning machine learning
What are some alternatives?
MLflow - Open source platform for the machine learning lifecycle
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
mlops-with-vertex-ai - An end-to-end example of MLOps on Google Cloud using TensorFlow, TFX, and Vertex AI
pyscaffoldext-dsproject - 💫 PyScaffold extension for data-science projects
TensorRT - PyTorch/TorchScript/FX compiler for NVIDIA GPUs using TensorRT
FastAPI-template - Feature rich robust FastAPI template.
machine-learning-interview - Machine Learning Interviews from FAANG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.
projects - Sample projects using Ploomber.
ML-Workspace - 🛠 All-in-one web-based IDE specialized for machine learning and data science.
jupyter - Jupyter metapackage for installation, docs and chat
fastai - The fastai deep learning library