MLOps
Made-With-ML
Our great sponsors
| MLOps | Made-With-ML | |
|---|---|---|
| 2 | 51 | |
| 1,709 | 35,656 | |
| 10.4% | - | |
| 2.5 | 6.8 | |
| 9 months 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.
MLOps
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Deploying Azure Machine Learning Models to Prod Environments
Walk through this, it shows how to operationalise your ML pipeline https://github.com/Microsoft/MLOps
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[D] How to maintain ML models?
Maybe something like this: https://github.com/microsoft/MLOps
Made-With-ML
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[D] How do you keep up to date on Machine Learning?
Made With ML
- Open-Source Production Machine Learning Course
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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.
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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
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Need help to find resources to learn ml ops
Try replicating this setup: https://madewithml.com/
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MLops Resources
madewithml
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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
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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?
MLflow - Open source platform for the machine learning lifecycle
zero-to-mastery-ml - All course materials for the Zero to Mastery Machine Learning and Data Science course.
dvc - 🦉 ML Experiments and Data Management with Git
mlops-zoomcamp - Free MLOps course from DataTalks.Club
mlops-with-vertex-ai - An end-to-end example of MLOps on Google Cloud using TensorFlow, TFX, and Vertex AI
FLAML - A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
pytorch-deepdream - PyTorch implementation of DeepDream algorithm (Mordvintsev et al.). Additionally I've included playground.py to help you better understand basic concepts behind the algo.
mlops-course - Learn how to design, develop, deploy and iterate on production-grade ML applications.
mllint - `mllint` is a command-line utility to evaluate the technical quality of Python Machine Learning (ML) projects by means of static analysis of the project's repository.
practical-mlops-book - [Book-2021] Practical MLOps O'Reilly Book
awesome-seml - A curated list of articles that cover the software engineering best practices for building machine learning applications.
Copulas - A library to model multivariate data using copulas.