ml-mipt
Made-With-ML
| ml-mipt | Made-With-ML | |
|---|---|---|
| 18 | 51 | |
| 8 | 35,702 | |
| - | - | |
| 0.0 | 6.8 | |
| over 1 year ago | 5 months ago | |
| Jupyter Notebook | 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.
ml-mipt
Made-With-ML
-
[D] How do you keep up to date on Machine Learning?
Made With ML
- Open-Source Production Machine Learning Course
-
Advice for switching careers within analytics
- Develop a (simple!) ML project and apply MLOps best practices to it. Ask Chat GPT all of your MLOps questions. I've joined this MLOps community and it has been very helpful to know what path to follow in order to be better at MLOps, thanks to them I arrived at madewithml, but I haven't done it yet. But it covers all the MLOps side.
-
Recommendation for MLOps resources
Hey, I’m also working in ML. Here’s a great resource: https://madewithml.com. Also, check out Noah Gift’s book Practical MLOPs.
- Ask HN: Resource to learn how to train and use ML Models
-
Need help to find resources to learn ml ops
Try replicating this setup: https://madewithml.com/
-
MLops Resources
madewithml
-
Ask HN: How do I get started with MLOps?
There's a really nice website by Goku Mohandas called Made With ML. IMO it is the best practical guide to MLOps out there: https://madewithml.com
Incase you want to dive a little deeper, https://fullstackdeeplearning.com/course/2022/ is also something I have been recommended by folks.
- Resources for Current DE Interested in Learning Data Science
-
Do organizations still need machine learning engineers?
madewithml is pretty sweet, especially the MLOps side of things. It'll give you good skills in how development in Python and deploying ML works.
What are some alternatives?
Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning - My Computer Vision project from my Computer Vision Course (Fall 2020) at Goethe University Frankfurt, Germany. Performance comparison between state-of-the-art Object Detection algorithms YOLO and Faster R-CNN based on the Berkeley DeepDrive (BDD100K) Dataset.
zero-to-mastery-ml - All course materials for the Zero to Mastery Machine Learning and Data Science course.
MachineLearningWithPython - Get started with Machine Learning with Python - An introduction with Python programming examples
mlops-zoomcamp - Free MLOps course from DataTalks.Club
mlops-course - Learn how to design, develop, deploy and iterate on production-grade ML applications.
FLAML - A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
pytorch-implementations - A collection of paper implementations using the PyTorch framework
Deep-Learning-Computer-Vision - My assignment solutions for Stanford’s CS231n (CNNs for Visual Recognition) and Michigan’s EECS 498-007/598-005 (Deep Learning for Computer Vision), version 2020.
practical-mlops-book - [Book-2021] Practical MLOps O'Reilly Book
d2l-en - Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
Copulas - A library to model multivariate data using copulas.