Feast alternatives for AI agents.

📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.

Open-source, low-code AutoML platform for Python. PyCaret 4.0: sklearn-native engine + React control plane.

🧙 Build, run, and manage data pipelines for integrating and transforming data.

Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.

12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all

Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.

Always know what to expect from your data.

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Deep Learning for humans

Ultralytics YOLOv5 in PyTorch > ONNX > CoreML > TFLite

💫 Industrial-strength Natural Language Processing (NLP) in Python

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

Learn how to design, develop, deploy and iterate on production-grade ML applications.