Search Results for "machine learning platform" - Page 7
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Showing 2549 open source projects for "machine learning platform"
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Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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AI_Tutorial
A selection of learning materials, search, recommendation, advertising
AI_Tutorial is a large curated repository that aggregates high-quality learning resources related to artificial intelligence, machine learning, deep learning, natural language processing, and data engineering. The project functions as a centralized knowledge base designed to help engineers and researchers discover tutorials, technical articles, algorithm explanations, and architecture discussions from across the AI ecosystem. -
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oneDNN
oneAPI Deep Neural Network Library (oneDNN)
This software was previously known as Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) and Deep Neural Network Library (DNNL). oneAPI Deep Neural Network Library (oneDNN) is an open-source cross-platform performance library of basic building blocks for deep learning applications. oneDNN is part of oneAPI. The library is optimized for Intel(R) Architecture Processors, Intel Processor Graphics and Xe Architecture graphics. oneDNN has experimental support for the following architectures: Arm* 64-bit Architecture (AArch64), NVIDIA* GPU, OpenPOWER* Power ISA (PPC64), IBMz* (s390x), and RISC-V. oneDNN is intended for deep learning applications and framework developers interested in improving application performance on Intel CPUs and GPUs. ... -
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Jina-Serve
Build multimodal AI applications with cloud-native stack
...Jina Serve focuses on making it easier to turn machine learning models into production-ready services without forcing developers to manage complex infrastructure manually. The framework supports many major machine learning libraries and data types, making it suitable for multimodal AI systems that process text, images, audio, and other inputs. -
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Data Version Control
Git-based data version control for machine learning workflows
DVC (Data Version Control) is an open source tool designed to bring version control principles to machine learning and data science workflows. It enables developers and data scientists to track datasets, machine learning models, and experiment results in a way that integrates with existing Git repositories. Instead of storing large datasets directly in Git, DVC keeps lightweight metadata in the repository while storing the actual data in external storage systems. ... -
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AutoMLPipeline.jl
Package that makes it trivial to create and evaluate machine learning
...It leverages on the built-in macro programming features of Julia to symbolically process, and manipulate pipeline expressions and makes it easy to discover optimal structures for machine learning regression and classification. To illustrate, here is a pipeline expression and evaluation of a typical machine learning workflow that extracts numerical features (numf) for ica (Independent Component Analysis) and pca (Principal Component Analysis) transformations, respectively, concatenated with the hot-bit encoding (ohe) of categorical features (catf) of a given data for rf (Random Forest) modeling. -
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Stock prediction deep neural learning
Predicting stock prices using a TensorFlow LSTM
...The fluctuations in stock prices are driven by the forces of supply and demand, which can be unpredictable at times. To identify patterns and trends in stock prices, deep learning techniques can be used for machine learning. Long short-term memory (LSTM) is a type of recurrent neural network (RNN) that is specifically designed for sequence modeling and prediction. -
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MCiSEE
All of Minecraft, EASILY get Minecraft resources
MCiSEE is an open-source project designed to integrate Minecraft with computer vision and artificial intelligence experiments. The system focuses on capturing visual information from the game environment and exposing it to external programs for analysis or machine learning research. By converting gameplay data into visual or structured formats, MCiSEE enables researchers and developers to build AI agents capable of interacting with the Minecraft environment. The project can be used as a research platform for tasks such as reinforcement learning, perception analysis, or automated gameplay strategies. ... -
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Spice.ai OSS
A self-hostable CDN for databases
...You can co-locate a managed dataset with your application or machine learning model, and accelerate it with Arrow in-memory, SQLite/DuckDB, or with attached PostgreSQL for fast, high-concurrency, low-latency queries. Accelerated engines give you flexibility and control over query cost and performance. -
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SHAP
A game theoretic approach to explain the output of ml models
SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions. While SHAP can explain the output of any machine learning model, we have developed a high-speed exact algorithm for tree ensemble methods. Fast C++ implementations are supported for XGBoost, LightGBM, CatBoost, scikit-learn and pyspark tree models. ... -
Stop Cyber Threats with VM-Series Next-Gen Firewall on AzureGain integrated visibility across all traffic in a single pass. Deploy Palo Alto Networks VM-Series to determine application identity and content while automating security policy updates via rich APIs.
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Google Cloud Platform Go Samples
Sample apps and code written for Google Cloud
Google Cloud Platform Go Samples repository is a comprehensive collection of Go-based code examples that demonstrate how to build applications and services using Google Cloud Platform. It provides developers with practical implementations that cover a wide spectrum of cloud functionalities, including storage, compute, networking, and machine learning services. -
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GROBID
A machine learning software for extracting information
GROBID is a machine learning library for extracting, parsing, and re-structuring raw documents such as PDF into structured XML/TEI encoded documents with a particular focus on technical and scientific publications. First developments started in 2008 as a hobby. In 2011 the tool has been made available in open source. Work on GROBID has been steady as a side project since the beginning and is expected to continue as such. -
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DeepDetect
Deep Learning API and Server in C++14 support for Caffe, PyTorch
The core idea is to remove the error sources and difficulties of Deep Learning applications by providing a safe haven of commoditized practices, all available as a single core. While the Open Source Deep Learning Server is the core element, with REST API, and multi-platform support that allows training & inference everywhere, the Deep Learning Platform allows higher level management for training neural network models and using them as if they were simple code snippets. ... -
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Gradio
Create UIs for your machine learning model in Python in 3 minutes
Gradio is the fastest way to demo your machine learning model with a friendly web interface so that anyone can use it, anywhere! Gradio can be installed with pip. Creating a Gradio interface only requires adding a couple lines of code to your project. You can choose from a variety of interface types to interface your function. Gradio can be embedded in Python notebooks or presented as a webpage. -
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AWS Neuron
Powering Amazon custom machine learning chips
AWS Neuron is a software development kit (SDK) for running machine learning inference using AWS Inferentia chips. It consists of a compiler, run-time, and profiling tools that enable developers to run high-performance and low latency inference using AWS Inferentia-based Amazon EC2 Inf1 instances. Using Neuron developers can easily train their machine learning models on any popular framework such as TensorFlow, PyTorch, and MXNet, and run it optimally on Amazon EC2 Inf1 instances. ... -
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DeepCTR-Torch
Easy-to-use,Modular and Extendible package of deep-learning models
DeepCTR-Torch is an easy-to-use, Modular and Extendible package of deep-learning-based CTR models along with lots of core components layers that can be used to build your own custom model easily.It is compatible with PyTorch.You can use any complex model with model.fit() and model.predict(). With the great success of deep learning, DNN-based techniques have been widely used in CTR estimation tasks. The data in the CTR estimation task usually includes high sparse,high cardinality categorical features and some dense numerical features. ... -
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Responsible AI Toolbox
Responsible AI Toolbox is a suite of tools providing model
Responsible AI Toolbox is a software framework designed to help developers evaluate and improve the reliability, fairness, and transparency of machine learning systems. The project provides tools that assist in analyzing model behavior, detecting bias, improving robustness, and explaining predictions produced by AI systems. It is designed to integrate with common machine learning frameworks, especially PyTorch, allowing developers to apply responsible AI techniques within existing workflows. ... -
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hls4ml
Machine learning on FPGAs using HLS
hls4ml is an open-source framework that enables machine learning models to be implemented directly on hardware such as FPGAs and ASICs using high-level synthesis techniques. The system converts trained neural network models from common machine learning frameworks into hardware description code suitable for ultra-low-latency inference. This approach allows machine learning algorithms to run directly on specialized hardware, making them suitable for applications that require extremely fast response times and minimal power consumption. ... -
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sktime
A unified framework for machine learning with time series
sktime is a library for time series analysis in Python. It provides a unified interface for multiple time series learning tasks. Currently, this includes time series classification, regression, clustering, annotation, and forecasting. It comes with time series algorithms and scikit-learn compatible tools to build, tune and validate time series models. Our objective is to enhance the interoperability and usability of the time series analysis ecosystem in its entirety. sktime provides a... -
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Kubeflow Trainer
Distributed AI Model Training and LLM Fine-Tuning on Kubernetes
Kubeflow Trainer is a Kubernetes-native platform designed for scalable, distributed training and fine-tuning of machine learning models, particularly large language models, across multi-node and multi-GPU environments. It extends the Kubeflow ecosystem by providing a unified framework for orchestrating training workloads using Kubernetes primitives, enabling seamless scaling from single-machine experiments to large production clusters. -
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FiftyOne
The open-source tool for building high-quality datasets
The open-source tool for building high-quality datasets and computer vision models. Nothing hinders the success of machine learning systems more than poor-quality data. And without the right tools, improving a model can be time-consuming and inefficient. FiftyOne supercharges your machine learning workflows by enabling you to visualize datasets and interpret models faster and more effectively. Improving data quality and understanding your model’s failure modes are the most impactful ways to boost the performance of your model. ... -
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Zingg
Scalable master data management and identity resolution
Zingg is an open-source entity resolution and master data management platform for finding duplicate, related, or matching records across large datasets. It uses machine learning to learn how records should be compared, reducing the need for brittle hand-written matching rules. The project is designed for data engineering and analytics teams working on customer 360, supplier 360, deduplication, fuzzy matching, data quality, and golden record workflows. -
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AI Engineer Headquarters
A collection of scientific methods, processes, algorithms
...The curriculum includes a progression of topics such as foundational AI engineering skills, machine learning systems design, large language model usage, retrieval-augmented generation systems, model fine-tuning, and autonomous AI agents. It also promotes disciplined learning routines and project-based practice so learners can develop practical experience and build deployable solutions. -
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ktrain
ktrain is a Python library that makes deep learning AI more accessible
ktrain is a Python library that makes deep learning and AI more accessible and easier to apply. ktrain is a lightweight wrapper for the deep learning library TensorFlow Keras (and other libraries) to help build, train, and deploy neural networks and other machine learning models. Inspired by ML framework extensions like fastai and ludwig, ktrain is designed to make deep learning and AI more accessible and easier to apply for both newcomers and experienced practitioners. ... -
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FSRS4Anki
A modern Anki custom scheduling based on Free Spaced Repetition
A modern spaced-repetition scheduler for Anki based on the Free Spaced Repetition Scheduler algorithm. -
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BudouX
Standalone, small, language-neutral
Standalone. Small. Language-neutral. BudouX is the successor to Budou, the machine learning-powered line break organizer tool. It is standalone. It works with no dependency on third-party word segmenters such as Google cloud natural language API. It is small. It takes only around 15 KB including its machine learning model. It's reasonable to use it even on the client-side. It is language-neutral. You can train a model for any language by feeding a dataset to BudouX’s training script.
