Streamlit Alternatives

At Posit, our goal is to make data science more open, intuitive, accessible, and collaborative. We provide tools that make it easy for individuals, teams, and enterprises to leverage powerful analytics and gain the insights they need to make a lasting impact. From the beginning, we’ve invested in open-source software like the RStudio IDE, Shiny, and tidyverse. Because we believe in putting the power of data science tools in the hands of everyone. We develop R and Python-based tools to help you produce higher-quality analysis faster. Securely share data-science applications across your team and the enterprise. Our code is your code. Build on it. Share it. Improve people’s lives with it. Take the time and effort out of uploading, storing, accessing, and sharing your work. We love hearing about the amazing work being done with our tools around the world. And we really love sharing those stories.

Flask is a lightweight WSGI web application framework. It is designed to make getting started quick and easy, with the ability to scale up to complex applications. It began as a simple wrapper around Werkzeug and Jinja and has become one of the most popular Python web application frameworks. Flask offers suggestions, but doesn't enforce any dependencies or project layout. It is up to the developer to choose the tools and libraries they want to use. There are many extensions provided by the community that make adding new functionality easy.

Build & Share Delightful Machine Learning Apps. 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. A Gradio interface can automatically generate a public link you can share with colleagues that lets them interact with the model on your computer remotely from their own devices. Once you've created an interface, you can permanently host it on Hugging Face. Hugging Face Spaces will host the interface on its servers and provide you with a link you can share.

Build and distribute any web application you can imagine. VIKTOR is the development platform for the engineering and construction industry. Empower your organisation to build and distribute scalable applications. Enter into a new era in engineering. Use our digital building blocks to rapidly create professional web-based applications and share them with everyone you want. VIKTOR is the most used application development platform in the engineering and construction industry. It enables engineers to quickly build their own software solutions and share them with everyone. Engineers and other domain experts know your business best. Create an agile organization by empowering them to adopt new technologies and rapidly create, test, distribute, and scale new software solutions according to their needs. This results in better solutions, high adoption rates, and lower development costs.

Retool is an application development platform that enables developers to combine the benefits of traditional software development with a drag-and-drop UI editor and AI to build internal tools radically faster. Building in Retool fits with how you develop software today—deploy it anywhere, connect to any internal service, import your libraries, debug with your toolchain, and share it securely to users—ensuring good and well-governed software by default. Retool is used by industry leaders such as Amazon, American Express, DoorDash, OpenAI, and Mercedes Benz for mission critical custom software across operations, billing, and customer support.

Horovod was originally developed by Uber to make distributed deep learning fast and easy to use, bringing model training time down from days and weeks to hours and minutes. With Horovod, an existing training script can be scaled up to run on hundreds of GPUs in just a few lines of Python code. Horovod can be installed on-premise or run out-of-the-box in cloud platforms, including AWS, Azure, and Databricks. Horovod can additionally run on top of Apache Spark, making it possible to unify data processing and model training into a single pipeline. Once Horovod has been configured, the same infrastructure can be used to train models with any framework, making it easy to switch between TensorFlow, PyTorch, MXNet, and future frameworks as machine learning tech stacks continue to evolve.

Alpine is a rugged, minimal tool for composing behavior directly in your markup. Think of it like jQuery for the modern web. Plop in a script tag and get going. Declare a new Alpine component and its data for a block of HTML. Dynamically set HTML attributes on an element. Prevent a block of HTML from being initialized by Alpine. Hide a block of HTML until after Alpine is finished initializing its contents. Reference elements directly by their specified keys using the magic property. Execute a script each time one of its dependencies change. Run code when an element is initialized by Alpine.

Bring data analysts, engineers, and scientists together. Enable self-service analytics and operationalize machine learning. Get results today and build for tomorrow. Dataiku DSS is the collaborative data science software platform for teams of data scientists, data analysts, and engineers to explore, prototype, build, and deliver their own data products more efficiently. Use notebooks (Python, R, Spark, Scala, Hive, etc.) or a customizable drag-and-drop visual interface at any step of the predictive dataflow prototyping process – from wrangling to analysis to modeling. Profile the data visually at every step of the analysis. Interactively explore and chart your data using 25+ built-in charts. Prepare, enrich, blend, and clean data using 80+ built-in functions. Leverage Machine Learning technologies (Scikit-Learn, MLlib, TensorFlow, Keras, etc.) in a visual UI. Build & optimize models in Python or R and integrate any external ML library through code APIs.

Switch between command and editor modes with a single keystroke. Navigate over cells with arrow keys. Use all of the standard Jupyter shortcuts. Enjoy fully interactive outputs – right under the cell. When editing code cells, enjoy smart code completion, on-the-fly error checking and quick-fixes, easy navigation, and much more. Work with local Jupyter notebooks or connect easily to remote Jupyter, JupyterHub, or JupyterLab servers right from the IDE. Run Python scripts or arbitrary expressions interactively in a Python Console. See the outputs and the state of variables in real-time. Split Python scripts into code cells with the #%% separator and run them individually as you would in a Jupyter notebook. Browse DataFrames and visualizations right in place via interactive controls. All popular Python scientific libraries are supported, including Plotly, Bokeh, Altair, ipywidgets, and others.

Build, run and manage AI models, and optimize decisions at scale across any cloud. IBM Watson Studio empowers you to operationalize AI anywhere as part of IBM Cloud Pak® for Data, the IBM data and AI platform. Unite teams, simplify AI lifecycle management and accelerate time to value with an open, flexible multicloud architecture. Automate AI lifecycles with ModelOps pipelines. Speed data science development with AutoAI. Prepare and build models visually and programmatically. Deploy and run models through one-click integration. Promote AI governance with fair, explainable AI. Drive better business outcomes by optimizing decisions. Use open source frameworks like PyTorch, TensorFlow and scikit-learn. Bring together the development tools including popular IDEs, Jupyter notebooks, JupterLab and CLIs — or languages such as Python, R and Scala. IBM Watson Studio helps you build and scale AI with trust and transparency by automating AI lifecycle management.

The RAPIDS suite of software libraries, built on CUDA-X AI, gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposes that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces. RAPIDS also focuses on common data preparation tasks for analytics and data science. This includes a familiar DataFrame API that integrates with a variety of machine learning algorithms for end-to-end pipeline accelerations without paying typical serialization costs. RAPIDS also includes support for multi-node, multi-GPU deployments, enabling vastly accelerated processing and training on much larger dataset sizes. Accelerate your Python data science toolchain with minimal code changes and no new tools to learn. Increase machine learning model accuracy by iterating on models faster and deploying them more frequently.

DSVMs are Azure Virtual Machine images, pre-installed, configured and tested with several popular tools that are commonly used for data analytics, machine learning and AI training. Consistent setup across team, promote sharing and collaboration, Azure scale and management, Near-Zero Setup, full cloud-based desktop for data science. Quick, Low friction startup for one to many classroom scenarios and online courses. Ability to run analytics on all Azure hardware configurations with vertical and horizontal scaling. Pay only for what you use, when you use it. Readily available GPU clusters with Deep Learning tools already pre-configured. Examples, templates and sample notebooks built or tested by Microsoft are provided on the VMs to enable easy onboarding to the various tools and capabilities such as Neural Networks (PYTorch, Tensorflow, etc.), Data Wrangling, R, Python, Julia, and SQL Server.

Deepnote is building the best data science notebook for teams. In the notebook, users can connect their data, explore, and analyze it with real-time collaboration and version control. Users can easily share project links with team collaborators, or with end-users to present polished assets. All of this is done through a powerful, browser-based UI that runs in the cloud. We built Deepnote because data scientists don't work alone. Features: - Sharing notebooks and projects via URL - Inviting others to view, comment and collaborate, with version control - Publishing notebooks with visualizations for presentations - Sharing datasets between projects - Set team permissions to decide who can edit vs view code - Full linux terminal access - Code completion - Automatic python package management - Importing from github - PostgreSQL DB connection

Hex brings together the best of notebooks, BI, and docs into a seamless, collaborative UI. Hex is a modern Data Workspace. It makes it easy to connect to data, analyze it in collaborative SQL and Python-powered notebooks, and share work as interactive data apps and stories. Your default landing page in Hex is the Projects page. You can quickly find projects you created, as well as those shared with you and your workspace. The outline provides an easy-to-browse overview of all the cells in a project's Logic View. Every cell in the outline lists the variables it defines, and cells that return a displayed output (chart cells, Input Parameters, markdown cells, etc.) display a preview of that output. You can click any cell in the outline to automatically jump to that position in the logic.

Bottle is a fast, simple and lightweight WSGI micro web-framework for Python. It is distributed as a single file module and has no dependencies other than the Python Standard Library. Requests to function-call mapping with support for clean and dynamic URLs. Fast and pythonic built-in template engine and support for mako, jinja2 and cheetah templates. Convenient access to form data, file uploads, cookies, headers and other HTTP-related metadata. Built-in HTTP development server and support for paste, bjoern, gae, cherrypy or any other WSGI capable HTTP server.

Builds on top of standard HTML, CSS and JavaScript with intuitive API and world-class documentation. Truly reactive, compiler-optimized rendering system that rarely requires manual optimization. A rich, incrementally adoptable ecosystem that scales between a library and a full-featured framework. Vue is a JavaScript framework for building user interfaces. It builds on top of standard HTML, CSS and JavaScript, and provides a declarative and component-based programming model that helps you efficiently develop user interfaces, be it simple or complex. Vue extends standard HTML with a template syntax that allows us to declaratively describe HTML output based on JavaScript state. Vue automatically tracks JavaScript state changes and efficiently updates the DOM when changes happen. Vue is a framework and ecosystem that covers most of the common features needed in frontend development.

Feathers can interact with any backend technology, supports many databases out of the box and works with any frontend technology like React, VueJS, Angular, React Native, Android or iOS. Build prototypes in minutes and production-ready apps in days. Leveraging a unique architecture, Feathers lets you focus on building your APIs and real-time applications quickly. You automatically get scalable HTTP and real-time APIs and stay prepared for whatever else the future might bring. Feathers can be used with NodeJS, in the browser, with React Native or with any other API client. You can use any database with many supports out of the box and connect your API seamlessly to any frontend framework. Built for TypeScript, Feathers provides the structure to create complex applications but is flexible enough to not be in the way. With a large ecosystem of plugins you can include exactly what you need.

Oorian is Java-based framework for creating dynamic, interactive, data-driven web applications purely in Java allowing you to leverage all the benefits of object-oriented design in your applications front-end to back-end. Your entire web application, including the UI, can be written in Java without the need for maintaining separate HTML, CSS, and/or Javascript code.

Sinatra includes a number of built-in settings that control whether certain features are enabled. Settings are application-level variables that are modified using one of the set, enable, or disable methods and are available within the request context via the settings object. Applications are free to set custom settings as well as the default, built-in settings provided by the framework. In its simplest form, the set method takes a setting name and value and creates an attribute on the application. Extensions provide helper or class methods for Sinatra applications. These methods are customarily listed and described on extensions home pages. Using an extension is usually as simple as installing a gem or library and requiring a file.

React makes it painless to create interactive UIs. Design simple views for each state in your application, and React will efficiently update and render just the right components when your data changes. Declarative views make your code more predictable and easier to debug. Build encapsulated components that manage their own state, then compose them to make complex UIs. Since component logic is written in JavaScript instead of templates, you can easily pass rich data through your app and keep state out of the DOM. We don’t make assumptions about the rest of your technology stack, so you can develop new features in React without rewriting existing code. React components implement a render() method that takes input data and returns what to display. This example uses an XML-like syntax called JSX. Input data that is passed into the component can be accessed by render() via this.props.

Build practical, production-ready Node.js apps in a matter of weeks, not months. Sails is the most popular MVC framework for Node.js, designed to emulate the familiar MVC pattern of frameworks like Ruby on Rails, but with support for the requirements of modern apps, data-driven APIs with scalable, service-oriented architecture. Sails makes it easy to build custom, enterprise-grade Node.js apps. Building on top of Sails means your app is written entirely in JavaScript, the language you and your team are already using in the browser. Sails bundles a powerful ORM, Waterline, which provides a simple data access layer that just works, no matter what database you're using. Sails comes with blueprints that help jumpstart your app's backend without writing any code. Since Sails translates incoming socket messages for you, they're automatically compatible with every route in your Sails app. Sails offers commercial support to accelerate development and ensure best practices in your code.

Daft is a framework for ETL, analytics and ML/AI at scale. Its familiar Python dataframe API is built to outperform Spark in performance and ease of use. Daft plugs directly into your ML/AI stack through efficient zero-copy integrations with essential Python libraries such as Pytorch and Ray. It also allows requesting GPUs as a resource for running models. Daft runs locally with a lightweight multithreaded backend. When your local machine is no longer sufficient, it scales seamlessly to run out-of-core on a distributed cluster. Daft can handle User-Defined Functions (UDFs) in columns, allowing you to apply complex expressions and operations to Python objects with the full flexibility required for ML/AI. Daft runs locally with a lightweight multithreaded backend. When your local machine is no longer sufficient, it scales seamlessly to run out-of-core on a distributed cluster.

Successful data science projects are delivered by data scientists who can build, improve, and operate end-to-end workflows independently, focusing more on data science, less on engineering. Use Metaflow with your favorite data science libraries, such as Tensorflow or SciKit Learn, and write your models in idiomatic Python code with not much new to learn. Metaflow also supports the R language. Metaflow helps you design your workflow, run it at scale, and deploy it to production. It versions and tracks all your experiments and data automatically. It allows you to inspect results easily in notebooks. Metaflow comes packaged with the tutorials, so getting started is easy. You can make copies of all the tutorials in your current directory using the metaflow command line interface.

Many Python frameworks can handle basic dashboards but falter with complex ones, often leading teams to split into frontend and backend roles, causing various challenges. Solara is a new web framework that integrates ReactJS principles with Python simplicity. It offers a flexible API for various UI complexities and efficient state management. Solara supports a range of applications, from prototypes to intricate dashboards, and is compatible with platforms like Jupyter, Voilà, and various web servers. It emphasizes code quality, developer accessibility, and robust testing.

Quadratic enables your team to work together on data analysis to deliver faster results. You already know how to use a spreadsheet, but you’ve never had this much power. Quadratic speaks Formulas and Python (SQL & JavaScript coming soon). Use the language you and your team already know. Single-line formulas are hard to read. In Quadratic you can expand your recipes to as many lines as you need. Quadratic has Python library support built-in. Bring the latest open-source tools directly to your spreadsheet. The last line of code is returned to the spreadsheet. Raw values, 1/2D arrays, and Pandas DataFrames are supported by default. Pull or fetch data from an external API, and it updates automatically in Quadratic's cells. Navigate with ease, zoom out for the big picture, and zoom in to focus on the details. Arrange and navigate your data how it makes sense in your head, not how a tool forces you to do it.

SAS® Viya® data science offerings provide a comprehensive, scalable analytics environment that's quick and easy to deploy, enabling you to meet diverse business needs. Automatically generated insights enable you to identify the most common variables across all models, the most important variables selected across models and assessment results for all models. Natural language generation capabilities are used to create project summaries written in plain language, enabling you to easily interpret reports. Analytics team members can add project notes to the insights report to facilitate communication and collaboration among team members. SAS lets you embed open source code within an analysis and call open source algorithms seamlessly within its environment. This facilitates collaboration across your organization because users can program in their language of choice. You can also take advantage of SAS Deep Learning with Python (DLPy), our open-source package on GitHub.

Maximize your security testing journey, from setting up your bench to analyzing your data processing results, esDynamic saves you valuable time and effort, empowering you to unleash the full potential of your attack workflow. Discover the flexible and comprehensive Python-based platform, perfectly suited for every phase of your security analysis. Customize your research space to meet your unique requirements by effortlessly adding new equipment, integrating tools, and modifying data. Additionally, esDynamic features an extensive collection of materials on complex topics that would typically require extensive research or a team of specialists, granting you instant access to expertise. Say goodbye to scattered data and fragmented knowledge. Welcome a cohesive workspace where your team can effortlessly share data and insights, fostering collaboration and accelerating discoveries. Centralize and solidify your efforts in JupyterLab notebooks to share with your team.

Saturn Cloud is an award-winning ML platform for any cloud with 100,000+ users, including NVIDIA, CFA Institute, Snowflake, Flatiron School, Nestle, and more. It is an all-in-one solution for data science & ML development, deployment, and data pipelines in the cloud. Users can spin up a notebook with 4TB of RAM, add a GPU, connect to a distributed cluster of workers, build large language models, and more in a completely hosted environment. Data professionals can use your preferred languages, IDEs, and machine-learning libraries in Saturn Cloud. We offer full Git integration, shared custom images, and secure credential storage, making scaling and building your team in the cloud easy. We support the entire machine learning lifecycle from experimentation to production with features like jobs and deployments. These features and built-in tools are easily shareable within teams, so time is saved and work is reproducible.

Sync, search and manage all the work across your data science team. Zepl’s powerful search lets you discover and reuse models and code. Use Zepl’s enterprise collaboration platform to query data from Snowflake, Athena or Redshift and build your models in Python. Use pivoting and dynamic forms for enhanced interactions with your data using heatmap, radar, and Sankey charts. Zepl creates a new container every time you run your notebook, providing you with the same image each time you run your models. Invite team members to join a shared space and work together in real time or simply leave their comments on a notebook. Use fine-grained access controls to share your work. Allow others have read, edit, and run access as well as enable collaboration and distribution. All notebooks are auto-saved and versioned. You can name, manage and roll back all versions through an easy-to-use interface, and export seamlessly into Github.

Falcon is a blazing fast, minimalist Python web API framework for building robust app backends and microservices. The framework works great with both asyncio (ASGI) and gevent/meinheld (WSGI). The Falcon web framework encourages the REST architectural style. Resource classes implement HTTP method handlers that resolve requests and perform state transitions. Falcon complements more general Python web frameworks by providing extra reliability, flexibility, and performance wherever you need it. A number of Falcon add-ons, templates, and complementary packages are available for use in your projects. We've listed several of these on the Falcon wiki as a starting point, but you may also wish to search PyPI for additional resources.

From simple pilots to production-ready web applications in no time. No more compromise on performance, customization, and scalability. Taipy enhances performance with caching control of graphical events, optimizing rendering by selectively updating graphical components only upon interaction. Effortlessly manage massive datasets with Taipy's built-in decimator for charts, intelligently reducing the number of data points to save time and memory without losing the essence of your data's shape. Struggle with sluggish performance and excessive memory usage, as every data point demands processing. Large datasets become cumbersome, complicating the user experience and data analysis. Scenarios are made easy with Taipy Studio. A powerful VS Code extension that unlocks a convenient graphical editor. Get your methods invoked at a certain time or intervals. Enjoy a variety of predefined themes or build your own.

A flexible visual query builder lets you get answers in seconds. Analyze data from your phone or browser to work from anywhere. Natural language querying, powered by LLMs lets you ask questions using plain English. No desktop, SQL, or data scientist needed. Shared questions let you learn from team mates, and search for any questions asked across your organization. @mentions, push notifications, and shared chat bring the right people into the conversation and empower you to make data actionable. Easily copy and modify shared questions, export data, and change how charts are displayed to not just view somebody elses’s analysis, but instead make it your own. You can even turn on external sharing to provide access to partners outside your domain or for public datasets. Get the underlying data tables in two taps. Even run full on custom SQL with smart typeaheads to make quick work of joins, aggregations, and calculated fields.

IBM SPSS Modeler is a leading visual data science and machine learning (ML) solution designed to help enterprises accelerate time to value by speeding up operational tasks for data scientists. Organizations worldwide use it for data preparation and discovery, predictive analytics, model management and deployment, and ML to monetize data assets. IBM SPSS Modeler automatically transforms data into the best format for the most accurate predictive modeling. It now only takes a few clicks for you to analyze data, identify fixes, screen out fields and derive new attributes. Leverage IBM SPSS Modeler’s powerful graphics engine to bring your insights to life. The smart chart recommender finds the perfect chart for your data from among dozens of options, so you can share your insights quickly and easily using compelling visualizations.

Brilent is a data science tech company developing a SaaS solution to help talent seekers quickly and effectively identify the right talent to hire. The exciting part about this intelligent technology is its simplicity. No tricks. It leverages the components recruiters find quite relevant. At the core of the Brilent engine are three simple elements: the job requirements; the candidate profile; and our unique repository of market data. Next comes the fun part. Our system gathers all the relevant data from the job requirements and candidate profiles. Using hundreds of variables from these familiar recruiting elements and the market data, we use our well-honed experience to apply artificial intelligence and machine learning algorithms to predict how likely a candidate is a good fit for a particular job. In other words, a whole lot of data crunching, and completed in seconds. Recruiters get a ranking of candidates according to the desired specifications.

Bitfount is a platform for distributed data science. We power deep data collaborations without data sharing. Distributed data science sends algorithms to data, instead of the other way around. Set up a federated privacy-preserving analytics and machine learning network in minutes, and let your team focus on insights and innovation instead of bureaucracy. Your data team has the skills to solve your biggest challenges and innovate, but they are held back by barriers to data access. Is complex data pipeline infrastructure messing with your plans? Are compliance processes taking too long? Bitfount has a better way to unleash your data experts. Connect siloed and multi-cloud datasets while preserving privacy and respecting commercial sensitivity. No expensive, time-consuming data lift-and-shift. Usage-based access controls to ensure teams only perform the analysis you want, on the data you want. Transfer management of access controls to the teams who control the data.

The entire process of building machine learning algorithms and predicting outcomes, packed in one single click. Not all data is built to be ready for ML, use the Data Dialog to seamlessly shape your dataset without wrangling your files. Share your prediction reports with your team or make them public. Allow anyone to start making predictions on your model. Bring dynamic ML predictions into your own app using our low-code API. Predict willingness to pay, score leads and much more in real-time. Obviously AI puts the world’s most cutting-edge algorithms in your hands, without compromising on performance. Forecast revenue, optimize supply chain, personalize marketing. You can now know what happens next. Add a CSV file OR integrate with your favorite data sources in minutes. Pick your prediction column from a dropdown, we'll auto build the AI. Beautifully visualize predicted results, top drivers and simulate "what-if" scenarios.

Datalore is a collaborative data science and analytics platform aimed at boosting the whole analytics workflow and making work with data enjoyable for both data scientists and data savvy business teams across the enterprise. Keeping a major focus on data teams workflow, Datalore offers technical-savvy business users the ability to work together with data teams, using no-code or low-code together with the power of Jupyter notebooks. Datalore enables analytical self-service for business users, enabling them to work with data using SQL and no-code cells, build reports and deep dive into data. It offloads the core data team with simple tasks. Datalore enables analysts and data scientists to share results with ML Engineers. You can run your code on powerful CPUs or GPUs and collaborate with your colleagues in real-time.

Growler is a web framework built atop asyncio, the asynchronous library described in PEP 3156 and added to the standard library in python 3.4. It takes a cue from the Connect & Express frameworks in the nodejs ecosystem, using a single application object and series of middleware to process HTTP requests. The custom chain of middleware provides an easy way to implement complex applications. The pip utility allows packages to provide optional requirements, so features may be installed only upon request. This meshes well with the minimal nature of the Growler project: don't install anything the user doesn't need. That being said, there are (will be) community packages that are blessed by the growler developers (after ensuring they work as expected and are well tested with each version of growler) that will be available as extras directly from the growler package.

Powerful, extensible, and feature-packed frontend toolkit. Build and customize with Sass, utilize prebuilt grid system and components, and bring projects to life with powerful JavaScript plugins. Jump right into building with Bootstrap, use the CDN, install it via the package manager, or download the source code. Bootstrap utilizes Sass for modular and customizable architecture. Import only the components you need, enable global options like gradients and shadows and write your own CSS with our variables, maps, functions, and mixins. Import one stylesheet and you're off to the races with every feature of our CSS. The easiest way to customize Bootstrap, include only the CSS you need. Bootstrap 5 is evolving with each release to better utilize CSS variables for global theme styles, individual components, and even utilities. We provide dozens of variables for colors, font styles, and more for use anywhere.

Gives you true flexibility by allowing use of any other libraries thanks to modular architecture. An adaptable ecosystem that is a fully-fledged backbone for all kinds of server-side applications. Takes advantage of latest JavaScript features, bringing design patterns and mature solutions to Node.js world. A complete development kit for building scalable server-side apps. In recent years, thanks to Node.js, JavaScript has become the “lingua franca” of the web for both front and backend applications. This has given rise to awesome projects like Angular, React and Vue, which improve developer productivity and enable the creation of fast, testable, and extensible frontend applications. However, while plenty of superb libraries, helpers, and tools exist for Node (and server-side JavaScript), none of them effectively solve the main problem of - Architecture.

Backbone.js gives structure to web applications by providing models with key-value binding and custom events, collections with a rich API of enumerable functions, views with declarative event handling, and connects it all to your existing API over a RESTful JSON interface. When working on a web application that involves a lot of JavaScript, one of the first things you learn is to stop tying your data to the DOM. It's all too easy to create JavaScript applications that end up as tangled piles of jQuery selectors and callbacks, all trying frantically to keep data in sync between the HTML UI, your JavaScript logic, and the database on your server. For rich client-side applications, a more structured approach is often helpful. With Backbone, you represent your data as Models, which can be created, validated, destroyed, and saved to the server.

JavaScript UI library and framework for speeding up web development. JS Framework for cross-platform web Apps development 102 UI widgets and feature-rich CSS / HTML5 JavaScript controls. Save at least 3000+ development hours by using ready-made widgets and UI controls. Develop Web UI 30% faster. We have accumulated the best design ideas. We have meticulously considered UX of each Webix component for five conceptual designs. Our support service specialists know everything about our library and can help you solve any problem. We also have an official support forum where you can discuss issues with our developers. By using Webix JS framework you receive an elegant and lightweight code based on object-oriented programming concepts. Associate your project with jQuery JavaScript library, MVC frameworks AngularJS, React, Vue.js, Backbone.js, third party UI extensions.

Svelte Native is a mobile application framework powered by Svelte, build mobile apps using the friendly web framework you already know. Build cross-platform, native iOS and Android apps without web views. Get truly native UI and performance while sharing skills and code with the web. Use the full power of Svelte including transitions, stores, and reactivity. One of the smoothest development experiences available for mobile. Svelte Native is a new approach to building mobile applications using NativeScript. Where other JavaScript mobile development frameworks like React Native and NativeScript-Vue do the bulk of their work on the mobile device, Svelte Native shifts that work into a compile step that happens when you build your app. Instead of using techniques like virtual DOM diffing, Svelte writes code that surgically updates the native view widgets when the state of your app changes.

Organize your app in terms of small Views. Marionette makes it easy to compose rich layouts out of small components. We've added tons of features from templateHelpers, to a declarative UI hash, that will keep you from ever wanting to go back. Share complex UI interactions across views. Behaviors are like mixins, without all of the pain associated with property collision. Decoupled communication between your application components with a powerful messaging system. Write classes with the same API as your views. Marionette Objects support features like extend, events, initialize, and more. Marionette community is home to the most welcoming and vibrant discussions in the Backbone ecosystem. Stop spending more time thinking about your framework than your app. Marionette will never get in the way of you and your code.

Creating ML apps should be simple and frictionless. UnionML is an open-source Python framework built on top of Flyte™, unifying the complex ecosystem of ML tools into a single interface. Combine the tools that you love using a simple, standardized API so you can stop writing so much boilerplate and focus on what matters: the data and the models that learn from them. Fit the rich ecosystem of tools and frameworks into a common protocol for machine learning. Using industry-standard machine learning methods, implement endpoints for fetching data, training models, serving predictions (and much more) to write a complete ML stack in one place. ‍ Data science, ML engineering, and MLOps practitioners can all gather around UnionML apps as a way of defining a single source of truth about your ML system’s behavior.

Apache Mahout is a powerful, scalable, and versatile machine learning library designed for distributed data processing. It offers a comprehensive set of algorithms for various tasks, including classification, clustering, recommendation, and pattern mining. Built on top of the Apache Hadoop ecosystem, Mahout leverages MapReduce and Spark to enable data processing on large-scale datasets. Apache Mahout(TM) is a distributed linear algebra framework and mathematically expressive Scala DSL designed to let mathematicians, statisticians, and data scientists quickly implement their own algorithms. Apache Spark is the recommended out-of-the-box distributed back-end or can be extended to other distributed backends. Matrix computations are a fundamental part of many scientific and engineering applications, including machine learning, computer vision, and data analysis. Apache Mahout is designed to handle large-scale data processing by leveraging the power of Hadoop and Spark.

Jest aims to work out of the box, config free, on most JavaScript projects. Make tests which keep track of large objects with ease. Snapshots live either alongside your tests, or embedded inline. Tests are parallelized by running them in their own processes to maximize performance. Tests are parallelized by running them in their own processes to maximize performance. By ensuring your tests have unique global state, Jest can reliably run tests in parallel. To make things quick, Jest runs previously failed tests first and re-organizes runs based on how long test files take. By ensuring your tests have unique global state, Jest can reliably run tests in parallel. To make things quick, Jest runs previously failed tests first and re-organizes runs based on how long test files take. Jest uses a custom resolver for imports in your tests, making it simple to mock any object outside of your test’s scope.

Learn one way to build applications with Angular and reuse your code and abilities to build apps for any deployment target. For web, mobile web, native mobile and native desktop. Achieve the maximum speed possible on the Web Platform today, and take it further, via Web Workers and server-side rendering. Angular puts you in control over scalability. Meet huge data requirements by building data models on RxJS, Immutable.js or another push-model. Build features quickly with simple, declarative templates. Extend the template language with your own components and use a wide array of existing components. Get immediate Angular-specific help and feedback with nearly every IDE and editor. All this comes together so you can focus on building amazing apps rather than trying to make the code work. From prototype through global deployment, Angular delivers the productivity and scalable infrastructure that supports Google's largest applications.

Core ML applies a machine learning algorithm to a set of training data to create a model. You use a model to make predictions based on new input data. Models can accomplish a wide variety of tasks that would be difficult or impractical to write in code. For example, you can train a model to categorize photos or detect specific objects within a photo directly from its pixels. After you create the model, integrate it in your app and deploy it on the user’s device. Your app uses Core ML APIs and user data to make predictions and to train or fine-tune the model. You can build and train a model with the Create ML app bundled with Xcode. Models trained using Create ML are in the Core ML model format and are ready to use in your app. Alternatively, you can use a wide variety of other machine learning libraries and then use Core ML Tools to convert the model into the Core ML format. Once a model is on a user’s device, you can use Core ML to retrain or fine-tune it on-device.

An entity framework for Go. Simple, yet powerful ORM for modeling and querying data. Simple API for modeling any database schema as Go objects. Run queries, and aggregations and traverse any graph structure easily. 100% statically typed and explicit API using code generation. The latest version of Ent now includes a type-safe API enabling ordering by fields and edges. This API will soon be available in our GraphQL integration too. You can now visualize your Ent schema as an ERD with one command. The API enables you to easily integrate features such as logging, tracing, caching, and even implementing soft deletion with 20 lines of code! The Ent framework supports GraphQL using the 99designs/gqlgen library and provides various integrations. Generating a GraphQL schema for nodes and edges defined in an Ent schema. Efficient field collection to overcome the N+1 problem without requiring data loaders.