Search Results for "machine learning platform"
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Showing 18 open source projects for "machine learning platform"
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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Forever Free Full-Stack Observability | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
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Uno Platform
Build Mobile, Desktop and WebAssembly apps
...Free and paid support is available by both community and our team. Chats, tickets or even screen sharing! Seeing is believing. Learn by doing. See code samples our engineers put out for fun, learning and reuse. Explore the possibilities Uno Platform opens. -
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dlib
Toolkit for making machine learning and data analysis applications
Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. It is used in both industry and academia in a wide range of domains including robotics, embedded devices, mobile phones, and large high performance computing environments. Dlib's open source licensing allows you to use it in any application, free of charge. -
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DALI
A GPU-accelerated library containing highly optimized building blocks
The NVIDIA Data Loading Library (DALI) is a library for data loading and pre-processing to accelerate deep learning applications. It provides a collection of highly optimized building blocks for loading and processing image, video and audio data. It can be used as a portable drop-in replacement for built-in data loaders and data iterators in popular deep learning frameworks. Deep learning applications require complex, multi-stage data processing pipelines that include loading, decoding,... -
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POCO
Cross-platform C++ libraries for building network applications
The POCO C++ Libraries are powerful cross-platform C++ libraries for building network- and internet-based applications that run on desktop, server, mobile, IoT, and embedded systems. Whether building automation systems, industrial automation, IoT platforms, air traffic management systems, enterprise IT application and infrastructure management, security and network analytics, automotive infotainment and telematics, financial or healthcare, C++ developers have been trusting the POCO C++... -
Fully Managed MySQL, PostgreSQL, and SQL ServerCloud SQL handles your database ops end to end, so you can focus on your app.
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Cake
Cake (C# Make) is a cross platform build automation system
...See Editors for a detailed feature list. Regardless if you're building on your own machine, or building on a CI system such as Azure Pipelines, GitHub Actions, TeamCity or Jenkins. -
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electron-builder
A complete solution to package and build a Electron app
A complete solution to package and build a ready-for-distribution Electron, Proton Native app for macOS, Windows and Linux with “auto update” support out of the box. You can use electron-builder only to pack your electron app in a AppImage, Snaps, Debian package, NSIS, macOS installer component package (pkg) and other distributable formats. Build and publish in parallel, using hard links on CI server to reduce IO and disk space usage. Docker images to build Electron app for Linux or Windows... -
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Luigi
Python module that helps you build complex pipelines of batch jobs
...You want to chain many tasks, automate them, and failures will happen. These tasks can be anything, but are typically long running things like Hadoop jobs, dumping data to/from databases, running machine learning algorithms, or anything else. You can build pretty much any task you want, but Luigi also comes with a toolbox of several common task templates that you use. It includes support for running Python mapreduce jobs in Hadoop, as well as Hive, and Pig, jobs. It also comes with file system abstractions for HDFS, and local files that ensures all file system operations are atomic. -
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SST
Build serverless apps. Set breakpoints and test your functions locally
...The sst start command starts up a local development environment that opens a WebSocket connection to your deployed app and proxies any Lambda requests to your local machine. Supports all Lambda triggers, so there's no need to mock API Gateway, SQS, SNS, etc. Supports real Lambda environment variables and Lambda IAM permissions. -
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FlubuCore
A cross platform build and deployment automation system
"FlubuCore - Fluent Builder Core" is a cross-platform build and deployment automation system. You can define your build and deployment scripts in C# using an intuitive fluent interface. This gives you code completion, IntelliSense, debugging, FlubuCore custom analyzers, and native access to the whole .NET ecosystem inside of your scripts. FlubuCore offers a .net (core) console application that uses power of roslyn to compile and execute scripts. Intuitive and easy to learn. C#, fluent... -
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
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Ruby 2D
The Ruby 2D gem
...Built atop SDL2 and OpenGL, Ruby2D abstracts away the complexity of low-level graphics programming while exposing enough control for performance and flexibility. It supports images, text, sounds, and basic geometric shapes, making it ideal for learning graphics or quickly prototyping ideas with Ruby. The library is cross-platform and lightweight, staying true to Ruby’s principle of developer happiness. -
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SageMaker MXNet Inference Toolkit
Toolkit for allowing inference and serving with MXNet in SageMaker
...The AWS DLCs are used in Amazon SageMaker as the default vehicles for your SageMaker jobs such as training, inference, transforms etc. They've been tested for machine learning workloads on Amazon EC2, Amazon ECS and Amazon EKS services as well. -
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pyTorch Tutorials
Build your neural network easy and fast
...The project is structured around clear, executable Python scripts and Jupyter notebooks that demonstrate regression, classification, convolutional networks, recurrent networks, autoencoders, and generative adversarial networks, which gives learners practical exposure to real machine learning tasks. Each example explains PyTorch’s dynamic computation graph, optimization techniques, and core abstractions in a way that is accessible and reproducible. Contributors and authors integrate visual and coded examples so readers can see both the theory and the implementation side-by-side. -
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AWS Step Functions Data Science SDK
For building machine learning (ML) workflows and pipelines on AWS
The AWS Step Functions Data Science SDK is an open-source library that allows data scientists to easily create workflows that process and publish machine learning models using Amazon SageMaker and AWS Step Functions. You can create machine learning workflows in Python that orchestrate AWS infrastructure at scale, without having to provision and integrate the AWS services separately. The best way to quickly review how the AWS Step Functions Data Science SDK works is to review the related example notebooks. ... -
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weekly-projects
Build a project every week to improve your coding skills
...Each weekly challenge includes a description, a set of user stories (tasks), and the necessary resources. After finishing, contributors can open a pull request with their own solution and demo link, so this becomes both a learning tool and a showcase platform. It’s great for those who want paced practice rather than open-ended ideas. The challenge list includes things like a Pokédex app, COVID tracker, movie app, quiz, daily planner, full-stack app, etc. The curated weekly cadence helps maintain consistency and momentum. And although lightweight, the public nature of submissions gives social accountability and visibility. -
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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. -
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X11workbench
X11 developer's 'workbench' and lightweight toolkit API
(preliminary) X11 developer's 'workbench' application using a lightweight statically linked custom toolkit that is intended to be used by X11 applications built with the X11 Workbench. The primary goal of the toolkit is ease of use (short learning curve), lightweight self-contained executables, UI speed, cross platform compatibility, and minimal dependencies. The primary goal of the workbench is to provide an editor on X11 platforms that integrates development, provides rapid development tools (like 'wizards' and safe X11 debugging), and allows you to move the cursor past the end of a line without creating 'end of line' whitespace or 'bouncing' the cursor horizontally while scrolling vertically. ... -
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Intelligent e-knowledge base engine for any kind of knowledge-based applications, supporting effective knowledge presentation, precise knowledge search, adaptive learning and immediate consulting.
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JaDiMa (Java Distributed Machine} is a collaborative platform to construct high performance distributed java applications on grid platforms. It is a system that automatically and transparently manages the remote libraries used in a java application.
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