The 30 Days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than 100 days. Follow your own pace. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw
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Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Streamlit — A faster way to build and share data apps.
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
Prefect is a workflow orchestration framework for building resilient data pipelines in Python.
An orchestration platform for the development, production, and observation of data assets.
🦉 Data Versioning and ML Experiments
1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
Always know what to expect from your data.
Low-code framework for building custom LLMs, neural networks, and other AI models
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
Fast and Accurate ML in 3 Lines of Code
Open Machine Learning Course
Open-source, low-code AutoML platform for Python. PyCaret 4.0: sklearn-native engine + React control plane.
Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀
An open source python library for automated feature engineering
The Open Source Feature Store for AI/ML
Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
🌊 Online machine learning in Python
ZenML 🙏: One AI Platform from Pipelines to Agents. https://zenml.io.
A library of extension and helper modules for Python's data analysis and machine learning libraries.
Lightning ⚡️ fast forecasting with statistical and econometric models.
Impress your boss and turn a Jupyter notebook into a beautiful, shareable web app — no callbacks, no frontend, no rewrite.