dagster-sklearn
best-of-ml-python
| dagster-sklearn | best-of-ml-python | |
|---|---|---|
| 3 | 16 | |
| 40 | 15,335 | |
| - | 0.7% | |
| 0.0 | 7.8 | |
| about 1 year ago | 7 days ago | |
| Python | Python | |
| - | Creative Commons Attribution Share Alike 4.0 |
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.
dagster-sklearn
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Scheduling tools for ETL and ML flow
I would give dagster a look. It has a built-in native scheduler and is cross-platform. It is general purpose, so your team can grow with it and tackle broader set of use cases if needed. If you struggle to get started after reading their docs/tutorials, you can take a look at my personal repo. Ive gotten a few feedback that my example has been very useful in getting started. I know they revamped their docs recently, but havent looked at their tutorial again or looked to see if they provided an intermediate level full example yet, so I need to get back in there to see.
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Dagster Tutorials/Presentations
Hey! I've recently started to use dagster and it's been great with its 0.11.x releases. I am still a newbie with it and maybe only use 20% of its features and abstractions. Here's my work-in-progress personal Github repo. Not sure if you'll learn much from it.
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Is anyone trying to switch out of data science, and if so, what jobs are you applying for?
I have created a trivial, contrived scikit-learn example using dagster so that people have an idea of how it can be used.
best-of-ml-python
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Ask HN: How to get back into AI?
For Python, here's a nice compilation: https://github.com/ml-tooling/best-of-ml-python/blob/main/RE...
- Best-Of Machine Learning with Python
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Questions regarding Job Requirements for data analyst to data science transition?
You will need numpy, scipy, pandas, scikit-learn, Keras/tensorflow/pytorch, xgboost and many many many others. See this list for example.
- Awesome list of ML
- Are there any speech recognition modules so I can write one and do not have to rely on google and the likes?
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Learning opencv
Take a look at this list on github. It has a pretty comprehensive list of python image libraries.
- Best-of Machine Learning with Python
- 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
What are some alternatives?
Dask - Parallel computing with task scheduling
Awesome-WAF - 🔥 Web-application firewalls (WAFs) from security standpoint.
dagster - An orchestration platform for the development, production, and observation of data assets.
ktrain - ktrain is a Python library that makes deep learning and AI more accessible and easier to apply
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
dtale - Visualizer for pandas data structures
yellowbrick - Visual analysis and diagnostic tools to facilitate machine learning model selection.
ffcv - FFCV: Fast Forward Computer Vision (and other ML workloads!)
dagster-example-pipeline - Template Dagster repo using poetry and a single Docker container; works well with CICD
awesome-python - An opinionated list of awesome Python frameworks, libraries, software and resources.
kmodes - Python implementations of the k-modes and k-prototypes clustering algorithms, for clustering categorical data
NBA-Machine-Learning-Sports-Betting - NBA sports betting using machine learning