Deep Learning for humans
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Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
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.
Prefect is a workflow orchestration framework for building resilient data pipelines in Python.
A reactive notebook for Python — run reproducible experiments, query with SQL, execute as a script, deploy as an app, and version with git. Stored as pure Python. All in a modern, AI-native editor.
An orchestration platform for the development, production, and observation of data assets.
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
Modin: Scale your Pandas workflows by changing a single line of code
Deep learning library featuring a higher-level API for TensorFlow.
[SIGMOD'27] Easy Data Preparation with latest LLMs-based Operators and Pipelines.
Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
ZenML 🙏: One AI Platform from Pipelines to Agents. https://zenml.io.
Koalas: pandas API on Apache Spark
CLI task management & automation tool
Python Stream Processing
:truck: Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark
NFStream: a Flexible Network Data Analysis Framework.
Pythonic tool for orchestrating machine-learning/high performance/quantum-computing workflows in heterogeneous compute environments.
Engine for AI/ML/Data tracking, visualization, explainability, drift detection, and dashboards for Polyaxon.
BatchFlow helps you conveniently work with random or sequential batches of your data and define data processing and machine learning workflows even for datasets that do not fit into memory.