Explore the ultimate Machine Learning Stack (Python, TensorFlow, Keras) to accelerate your journey in AI and deep learning. Discover the essential open source libraries, from data processing to model deployment, and stay ahead with the latest tools powering machine learning advancements in Python. Optimize your workflow with TensorFlow, Keras, and an extensive ecosystem of powerful libraries.
feature.delivery is a free, web-based platform that enables developers to monitor and consolidate software releases from multiple GitHub repositories into a single, streamlined chronological view. By centralizing release information across various tools, libraries, and services, feature.delivery simplifies the process of staying informed about the latest updates in a development stack. Stay ahead of the curve with feature.delivery, the free online tool designed to help developers effortlessly track and consolidate the latest releases from multiple GitHub repositories in one clean, chronological view. Whether you're managing a complex development stack or simply want to stay up to date with your favorite open-source projects, feature.delivery streamlines release tracking so you never miss an important update. By keeping up with the latest changes, developers can quickly adopt new features, enhance performance, and maintain a competitive edge in today's fast-moving tech landscape. Say goodbye to manual tracking and hello to smarter, faster development with feature.delivery.

https://feature.delivery

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SeldonIO/
alibi

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albumentations-team/
albumentations

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huggingface/
transformers

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keras-team/
keras

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keras-team/
keras-applications

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matplotlib/
matplotlib

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mlflow/
mlflow

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mwaskom/
seaborn

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numpy/
numpy

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onnx/
onnx

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pandas-dev/
pandas

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scikit-learn/
scikit-learn

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tensorflow/
datasets

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tensorflow/
hub

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tensorflow/
serving

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tensorflow/
tensorflow

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tensorflow/
text

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tensorflow/
tfx

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wandb/
client

Use this link to track latest updates across the 19 repositories in Machine Learning Stack

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Staying up-to-date with latest features of the
Machine Learning Stack in 2025

How does it work?

feature.delivery is a free, web-based platform that helps developers track the latest releases from multiple GitHub repositories — all in one streamlined, chronological view. By centralizing release information across tools, libraries, and frameworks, feature.delivery makes it easier than ever to stay on top of the updates throughout your development stack.

Checkout this 1 minute intro video to see it in action

The Machine Learning Stack (Python, TensorFlow, Keras) stack empowers developers and data scientists to efficiently build, train, and deploy advanced machine learning and deep learning models. Leveraging Python’s simplicity, TensorFlow’s scalability, and Keras’s ease of use, this stack offers an extensive ecosystem of tools, libraries, and frameworks, making it the preferred choice for both research and production machine learning workflows.

Here's a breakdown of the Machine Learning Stack into different categories

Core Machine Learning Libraries

These libraries are the backbone of the stack, providing the essential frameworks and APIs for developing, training, and deploying machine learning models.

TensorFlow

tensorflow/tensorflow
An end-to-end open source platform for machine learning, providing comprehensive tools and libraries.
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Keras

keras-team/keras
A high-level neural networks API, running on top of TensorFlow, focused on ease of use and fast experimentation.
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Scikit-learn

scikit-learn/scikit-learn
A robust machine learning library for Python, featuring simple and efficient tools for predictive data analysis.
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Data Processing & Manipulation

Libraries in this category facilitate cleaning, transforming, and visualizing data, which is crucial for machine learning workflows.

Pandas

pandas-dev/pandas
A fast, powerful data analysis and manipulation library built on top of Python.
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NumPy

numpy/numpy
The foundational package for numerical computing with Python, offering support for large, multi-dimensional arrays and matrices.
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Matplotlib

matplotlib/matplotlib
A comprehensive library for creating static, animated, and interactive visualizations in Python.
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Seaborn

mwaskom/seaborn
A statistical data visualization library built on top of Matplotlib, providing a high-level interface for drawing attractive graphs.
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Deep Learning Utilities

This category includes libraries that extend deep learning capabilities, such as model interpretability, advanced architectures, and transfer learning.

TensorFlow Hub

tensorflow/hub
A library for reusable machine learning modules, enabling seamless transfer learning and model sharing.
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Keras Applications

keras-team/keras-applications
Pre-trained deep learning models, ready to use for prediction, feature extraction, and fine-tuning.
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new features in Keras Applications?

Alibi

SeldonIO/alibi
Machine learning model inspection and interpretation for black-box models.
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new features in Alibi?

Data Loading & Augmentation

Tools in this category help with loading, preprocessing, and augmenting data, which is essential for robust machine learning pipelines.

TensorFlow Datasets

tensorflow/datasets
A collection of ready-to-use datasets for TensorFlow, supporting data loading, preprocessing, and augmentation.
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Albumentations

albumentations-team/albumentations
A fast and flexible image augmentation library, popular for deep learning tasks.
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Model Deployment

Deployment libraries make it easy to serve, monitor, and scale machine learning models into production environments.

TensorFlow Serving

tensorflow/serving
A flexible, high-performance serving system for machine learning models, designed for production environments.
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TFX (TensorFlow Extended)

tensorflow/tfx
An end-to-end platform for deploying production machine learning pipelines based on TensorFlow.
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ONNX

onnx/onnx
An open format for representing machine learning models, enabling interoperability between different frameworks.
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Experiment Tracking & Reproducibility

These tools assist in tracking experiments, managing versions, and ensuring reproducibility of machine learning workflows.

MLflow

mlflow/mlflow
An open source platform for managing the ML lifecycle, including experimentation, reproducibility, and deployment.
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new features in MLflow?

Weights & Biases

wandb/client
A tool for experiment tracking, model management, and collaboration in machine learning projects.
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Natural Language Processing

Specialized libraries and toolkits for building and deploying NLP models, from text preprocessing to deep learning for language tasks.

TensorFlow Text

tensorflow/text
TensorFlow integration of text processing tools for NLP tasks.
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Transformers

huggingface/transformers
State-of-the-art pre-trained models for NLP, including BERT, GPT, and more.
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Explore the latest releases and cutting-edge features of the Machine Learning Stack (Python, TensorFlow, Keras) by visiting these repositories. Click on the URLs to access powerful tools and stay up to date with the ongoing advancements in machine learning!