Search Results for "management framework"
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Showing 8 open source projects for "management framework"
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Metaflow
A framework for real-life data science
Metaflow is a human-friendly Python library that helps scientists and engineers build and manage real-life data science projects. Metaflow was originally developed at Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning. -
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XGBoost
Scalable and Flexible Gradient Boosting
XGBoost is an optimized distributed gradient boosting library, designed to be scalable, flexible, portable and highly efficient. It supports regression, classification, ranking and user defined objectives, and runs on all major operating systems and cloud platforms. XGBoost works by implementing machine learning algorithms under the Gradient Boosting framework. It also offers parallel tree boosting (GBDT, GBRT or GBM) that can quickly and accurately solve many data science problems.... -
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DearPyGui
Graphical User Interface Toolkit for Python with minimal dependencies
Dear PyGui is an easy-to-use, dynamic, GPU-Accelerated, cross-platform graphical user interface toolkit(GUI) for Python. It is “built with” Dear ImGui. Features include traditional GUI elements such as buttons, radio buttons, menus, and various methods to create a functional layout. Additionally, DPG has an incredible assortment of dynamic plots, tables, drawings, debuggers, and multiple resource viewers. DPG is well suited for creating simple user interfaces as well as developing complex... -
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AI Data Science Team
An AI-powered data science team of agents
AI Data Science Team is a Python library and agent ecosystem designed to accelerate and automate common data science workflows by modeling them as specialized AI “agents” that can be orchestrated to perform tasks like data cleaning, transformation, analysis, visualization, and machine learning. It provides a modular agent framework where each agent focuses on a step in the typical data science pipeline — for example, loading data from CSV/Excel files, cleaning and wrangling messy datasets,... -
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NVIDIA Merlin
Library providing end-to-end GPU-accelerated recommender systems
NVIDIA Merlin is an open-source library that accelerates recommender systems on NVIDIA GPUs. The library enables data scientists, machine learning engineers, and researchers to build high-performing recommenders at scale. Merlin includes tools to address common feature engineering, training, and inference challenges. Each stage of the Merlin pipeline is optimized to support hundreds of terabytes of data, which is all accessible through easy-to-use APIs. For more information, see NVIDIA... -
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TensorFlow.NET
.NET Standard bindings for Google's TensorFlow for developing models
TensorFlow.NET (TF.NET) provides a .NET Standard binding for TensorFlow. It aims to implement the complete Tensorflow API in C# which allows .NET developers to develop, train and deploy Machine Learning models with the cross-platform .NET Standard framework. TensorFlow.NET has built-in Keras high-level interface and is released as an independent package TensorFlow.Keras. SciSharp STACK's mission is to bring popular data science technology into the .NET world and to provide .NET developers... -
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Amazon SageMaker Examples
Jupyter notebooks that demonstrate how to build models using SageMaker
Welcome to Amazon SageMaker. This projects highlights example Jupyter notebooks for a variety of machine learning use cases that you can run in SageMaker. If you’re new to SageMaker we recommend starting with more feature-rich SageMaker Studio. It uses the familiar JupyterLab interface and has seamless integration with a variety of deep learning and data science environments and scalable compute resources for training, inference, and other ML operations. Studio offers teams and companies... -
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TensorWatch
Debugging, monitoring and visualization for Python Machine Learning
TensorWatch is an open source debugging and visualization platform created by Microsoft Research to support machine learning, deep learning, and reinforcement learning workflows. It enables developers to observe training behavior in real time through interactive visualizations, primarily within Jupyter Notebook environments. The tool treats most data interactions as streams, allowing flexible routing, storage, and visualization of metrics generated during model training. A distinctive...
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