Search Results for "as built"
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Showing 22 open source projects for "as built"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Full-stack observability with actually useful AI | 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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RStudio
RStudio is an integrated development environment (IDE) for R
...In addition to code editing and execution, RStudio offers extensive support for reproducible research via R Markdown, notebooks, and integration with version control systems like Git and SVN. Package development is built in, with tooling for building, checking, and testing R packages, plus integration with documentation tools, CRAN submission workflows, and project templates. -
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Positron
Positron, a next-generation data science IDE
...It aims to unify exploratory data analysis, production code, and data-app authoring in a single environment so that data scientists move from “question → insight → application” without switching tools. Built on the open-source Code-OSS foundation, Positron provides a familiar coding experience along with specialized panes and tooling for variable inspection, data-frame viewing, plotting previews, and interactive consoles designed for analytical work. The IDE supports notebook and script workflows, integration of data-app frameworks (such as Shiny, Streamlit, Dash), database and cloud connections, and built-in AI-assisted capabilities to help write code, explore data, and build models. -
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Milvus
Vector database for scalable similarity search and AI applications
Milvus is an open-source vector database built to power embedding similarity search and AI applications. Milvus makes unstructured data search more accessible, and provides a consistent user experience regardless of the deployment environment. Milvus 2.0 is a cloud-native vector database with storage and computation separated by design. All components in this refactored version of Milvus are stateless to enhance elasticity and flexibility. -
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Quadratic
Data science spreadsheet with Python & SQL
...The grid runs entirely in the browser with no backend service. This makes our grids completely portable and very easy to share. Quadratic has Python library support built-in. Bring the latest open-source tools directly to your spreadsheet. Quickly write code and see the output in full detail. No more squinting into a tiny terminal to see your data output. -
Application Monitoring That Won't Slow Your App DownFull APM with errors, performance, logs, and uptime monitoring. 99.999% uptime SLA on the platform itself.
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NVIDIA Merlin
Library providing end-to-end GPU-accelerated recommender systems
...Transform data (ETL) for preprocessing and engineering features. Accelerate your existing training pipelines in TensorFlow, PyTorch, or FastAI by leveraging optimized, custom-built data loaders. Scale large deep learning recommender models by distributing large embedding tables that exceed available GPU and CPU memory. Deploy data transformations and trained models to production with only a few lines of code. -
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cuDF
GPU DataFrame Library
Built based on the Apache Arrow columnar memory format, cuDF is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data. cuDF provides a pandas-like API that will be familiar to data engineers & data scientists, so they can use it to easily accelerate their workflows without going into the details of CUDA programming. -
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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 and demanding graphical interfaces. ... -
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NannyML
Detecting silent model failure. NannyML estimates performance
NannyML is an open-source python library that allows you to estimate post-deployment model performance (without access to targets), detect data drift, and intelligently link data drift alerts back to changes in model performance. Built for data scientists, NannyML has an easy-to-use interface, and interactive visualizations, is completely model-agnostic, and currently supports all tabular classification use cases. NannyML closes the loop with performance monitoring and post deployment data science, empowering data scientist to quickly understand and automatically detect silent model failure. ... -
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Catbird Linux
Linux for content creation, web scraping, coding, and data analysis.
Catbird Linux is a USB pluggable Live Linux operating system built for media creation, web scraping, and software coding. It is the daily driver you want for retrieving data, making videos or podcasts, and making software tools to automate the repetitive tasks. It is ready for work in Python, Lua, and Go languages, with numerous packages for web scraping or downloading data via API calls. Using Catbird Linux, it is possible to accomplish in depth stock market analysis, track weather trends, follow social media sentiment, or do other tasks in data science. ... -
Gemini 3 and 200+ AI Models on One PlatformBuild, govern, and optimize agents and models with Gemini Enterprise Agent Platform.
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MCPower
MCPower — simple Monte Carlo power analysis for complex models
...It guides users through the full workflow across three tabs: Model setup (formula input with live parsing, CSV data upload with auto-detected variable types, effect size sliders, and correlation editing), Analysis configuration (find power for a given sample size or find the minimum sample size for a target power, with multiple testing correction and scenario analysis), and Results (interactive charts, exportable tables, and auto-generated Python replication scripts). Supports both standard linear models and mixed-effects models. Additional features include analysis history, configurable scenarios, and built-in documentation. -
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sadsa
SADSA (Software Application for Data Science and Analytics)
SADSA (Software Application for Data Science and Analytics) is a Python-based desktop application designed to simplify statistical analysis, machine learning, and data visualization for students, researchers, and data professionals. Built using Python for the GUI, SADSA provides a menu-driven interface for handling datasets, applying transformations, running advanced statistical tests, machine learning algorithms, and generating insightful plots — all without writing code. -
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TensorFlow.NET
.NET Standard bindings for Google's TensorFlow for developing models
...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 with a powerful Machine Learning tool set without reinventing the wheel. Since the APIs are kept as similar as possible you can immediately adapt any existing TensorFlow code in C# or F# with a zero learning curve. ... -
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SageMaker Inference Toolkit
Serve machine learning models within a Docker container
...The SageMaker Inference Toolkit implements a model serving stack and can be easily added to any Docker container, making it deployable to SageMaker. This library's serving stack is built on Multi Model Server, and it can serve your own models or those you trained on SageMaker using machine learning frameworks with native SageMaker support. -
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Orchest
Build data pipelines, the easy way
...Python, R, Julia, JavaScript, and Bash are supported. Parameterize your pipelines and run them periodically on a cron schedule. Easily install language or system packages. Built on top of regular Docker container images. Creation of multiple instances with up to 8 vCPU & 32 GiB memory. A free Orchest instance with 2 vCPU & 8 GiB memory. Simple data pipelines with Orchest. Each step runs a file in a container. It's that simple! Spin up services whose lifetime spans across the entire pipeline run. Easily define your dependencies to run on any machine. ... -
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Deep Learning course
Slides and Jupyter notebooks for the Deep Learning lectures
...This course is being taught at as part of Master Year 2 Data Science IP-Paris. Note: press "P" to display the presenter's notes that include some comments and additional references. This lecture is built and maintained by Olivier Grisel and Charles Ollion. -
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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... -
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ML workspace
All-in-one web-based IDE specialized for machine learning
...The ML workspace is an all-in-one web-based IDE specialized for machine learning and data science. It is simple to deploy and gets you started within minutes to productively built ML solutions on your own machines. This workspace is the ultimate tool for developers preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch, Keras, Sklearn) and dev tools (e.g., Jupyter, VS Code, Tensorboard) perfectly configured, optimized, and integrated. Usable as remote kernel (Jupyter) or remote machine (VS Code) via SSH. ... -
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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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Deep Learning with PyTorch
Latest techniques in deep learning and representation learning
...To be able to follow the exercises, you are going to need a laptop with Miniconda (a minimal version of Anaconda) and several Python packages installed. The following instruction would work as is for Mac or Ubuntu Linux users, Windows users would need to install and work in the Git BASH terminal. JupyterLab has a built-in selectable dark theme, so you only need to install something if you want to use the classic notebook interface. -
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Seldon Server
Machine learning platform and recommendation engine on Kubernetes
Seldon Server is a machine learning platform and recommendation engine built on Kubernetes. Seldon reduces time-to-value so models can get to work faster. Scale with confidence and minimize risk through interpretable results and transparent model performance. Seldon Core focuses purely on deploying a wide range of ML models on Kubernetes, allowing complex runtime serving graphs to be managed in production. -
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DeepLearningProject
An in-depth machine learning tutorial
...In the fall of 2016, I was a Teaching Fellow (Harvard's version of TA) for the graduate class on "Advanced Topics in Data Science (CS209/109)" at Harvard University. I was in charge of designing the class project given to the students, and this tutorial has been built on top of the project I designed for the class. -
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Rodeo
A data science IDE for Python
...Rodeo makes it very easy for its users to explore what is created by them and also alongside allows the users to Inspect, interact, compare data frames, plots and even much more. It is an IDE that has been built especially for data science/Machine Learning in Python and you can also very simply think of it as a light weight alternative to the IPython Notebook.
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