Alternatives to Streamlit

Fully managed ML tools allow you to build, deploy and scale machine-learning (ML) models quickly, for any use case. Vertex AI Workbench is natively integrated with BigQuery Dataproc and Spark. You can use BigQuery to create and execute machine-learning models in BigQuery by using standard SQL queries and spreadsheets or you can export datasets directly from BigQuery into Vertex AI Workbench to run your models there. Vertex Data Labeling can be used to create highly accurate labels for data collection.

One source of truth for R packages and Python packages RStudio is the preferred professional data science solution for every group. A Python and R integrated development environment with syntax-highlighting editor, console, and code execution. It also includes tools for workspace management, history, plotting, and plotting. You can publish and distribute data products throughout your organization. One-button deployment of Shiny applications and R Markdown reports, Jupyter Notebooks, etc. To increase reproducibility and reduce the time spent installing and troubleshooting R packages, you can control, organize, and manage your use of them. RStudio is committed to sustainable investment in open-source and free software for data science. RStudio has been certified as a B Corporation. This means that our open-source mission has been codified in our charter. Our professional software products are enterprise-ready and provide a modular platform that allows teams to adopt open-source data sciences at scale.

Flask is a lightweight WSGI framework for web applications. It's easy to use and can scale up to complex applications. It started as a wrapper for Jinja and Werkzeug, and has since grown to be one of the most popular Python web app frameworks. Flask is a suggestion tool that doesn't require any dependencies. The developer can choose the tools and libraries that they prefer. The community has many extensions that make it easy to add new functionality.

Create & Share Delightful Apps for Machine Learning. Gradio allows you to quickly and easily demo your machine-learning model. It has a friendly interface that anyone can use, anywhere. Installing Gradio is easy with pip. It only takes a few lines of code to create a Gradio Interface. You can choose between a variety interface types to interface with your function. Gradio is available as a webpage or embedded into Python notebooks. Gradio can generate a link that you can share publicly with colleagues to allow them to interact with your model remotely using their own devices. Once you have created an interface, it can be permanently hosted on Hugging Face. Hugging Face Spaces hosts the interface on their servers and provides you with a shareable link.

You can build and distribute any type of web application. VIKTOR is the platform for the engineering, construction and construction industries. Your organisation can build and distribute scalable apps. Enter into a new era in engineering. Our digital building blocks allow you to quickly create web-based applications that are professional and easy to share with anyone. VIKTOR is the most popular application development platform in engineering and construction. It allows engineers to quickly create their own software solutions and share them easily with others. Engineers and other domain experts are the best people to know your business. You can empower your employees to adopt new technologies and quickly create, test, distribute and scale new software solutions to meet their needs. This leads to better solutions, higher adoption rates, and lower development cost.

Retool is a platform that enables developers to combine the benefits of traditional software development with a drag-and-drop editor and AI to build internal tools faster. Every tool can be deployed anywhere, debugged with your toolchain, and shared reliably at any scale—ensuring good software by default. Retool is used by industry leaders such as Amazon, American Express, and OpenAI for mission critical custom software across operations, billing, and customer support.

You can build, run, and manage AI models and optimize decisions across any cloud. IBM Watson Studio allows you to deploy AI anywhere with IBM Cloud Pak®, the IBM data and AI platform. Open, flexible, multicloud architecture allows you to unite teams, simplify the AI lifecycle management, and accelerate time-to-value. ModelOps pipelines automate the AI lifecycle. AutoAI accelerates data science development. AutoAI allows you to create and programmatically build models. One-click integration allows you to deploy and run models. Promoting AI governance through fair and explicable AI. Optimizing decisions can improve business results. Open source frameworks such as PyTorch and TensorFlow can be used, as well as scikit-learn. You can combine the development tools, including popular IDEs and Jupyter notebooks. JupterLab and CLIs. This includes languages like Python, R, and Scala. IBM Watson Studio automates the management of the AI lifecycle to help you build and scale AI with trust.

Data analysts, engineers, scientists, and other scientists can be brought together. Automate self-service analytics and machine learning operations. Get results today, build for tomorrow. Dataiku DSS is a collaborative data science platform that allows data scientists, engineers, and data analysts to create, prototype, build, then deliver their data products more efficiently. Use notebooks (Python, R, Spark, Scala, Hive, etc.) You can also use a drag-and-drop visual interface or Python, R, Spark, Scala, Hive notebooks at every step of the predictive dataflow prototyping procedure - from wrangling to analysis and modeling. Visually profile the data at each stage of the analysis. Interactively explore your data and chart it using 25+ built in charts. Use 80+ built-in functions to prepare, enrich, blend, clean, and clean your data. Make use of Machine Learning technologies such as Scikit-Learn (MLlib), TensorFlow and Keras. In a visual UI. You can build and optimize models in Python or R, and integrate any external library of ML through code APIs.

Built on top of standard HTML, CSS, and JavaScript with intuitive APIs and world-class documentation. Reactive, compiler-optimized rendering system that rarely needs manual optimization. An ecosystem rich in features that can be easily adopted incrementally. It can scale between a library or a fully-featured framework. Vue is a JavaScript framework that allows you to build user interfaces. It is built on top of JavaScript, CSS, and standard HTML and offers a declarative and component-based programming structure that allows you to efficiently create user interfaces, no matter how simple or complex. Vue extends the standard HTML with a template syntax, which allows us to describe HTML output using JavaScript state. Vue tracks JavaScript state changes automatically and updates the DOM as soon as they occur. Vue is a framework that provides most of the features required for frontend development.

Uber developed Horovod to make distributed deep-learning fast and easy to implement, reducing model training time from days and even weeks to minutes and hours. Horovod allows you to scale up an existing script so that it runs on hundreds of GPUs with just a few lines Python code. Horovod is available on-premises or as a cloud platform, including AWS Azure and Databricks. Horovod is also able to run on Apache Spark, allowing data processing and model-training to be combined into a single pipeline. Horovod can be configured to use the same infrastructure to train models using any framework. This makes it easy to switch from TensorFlow to PyTorch to MXNet and future frameworks, as machine learning tech stacks evolve.

Feathers works with any backend technology and supports many databases right out of the box. It also works with any frontend technologies like React, VueJS or Angular. Build prototypes within minutes and production-ready applications in days. Feathers' unique architecture lets you focus on quickly building real-time apps and APIs. You get scalable HTTP real-time APIs, and you're prepared for the future. Feathers is compatible with NodeJS in the browser, React Native, or any other API client. You can connect your API to any frontend framework and use any database that supports many features out of the box. Feathers is built for TypeScript and provides the structure needed to create complex applications, but it's flexible enough to avoid getting in the way. With a wide range of plugins, you can find exactly what you are looking for.

Deepnote is building the best data science notebook for teams. Connect your data, explore and analyze it within the notebook with real-time collaboration and versioning. Share links to your projects with other analysts and data scientists on your team, or present your polished, published notebooks to end users and stakeholders. All of this is done through a powerful, browser-based UI that runs in the cloud.

You can build practical, production-ready Node.js applications in a matter weeks, not months. Sails, the most popular MVC framework for Node.js is designed to mimic the familiar MVC pattern from frameworks like Ruby on Rails but with support for modern apps, data driven APIs with scalable and service-oriented architecture. Sails makes it easy for enterprises to create custom Node.js applications. Your app will be written entirely in JavaScript. This is the same language that you and your team use in the browser. Sails bundles Waterline, a powerful ORM that allows you to access data from any database. Sails comes with blueprints to help you jumpstart your app’s backend without having to write any code. Sails automatically translates socket messages for you. This makes them compatible with every route in your Sails App. Sails provides commercial support to help you accelerate your development and ensure the best practices in your code.

React makes it easy to create interactive user interfaces. React makes it easy to create simple views for each state of your application. React will automatically update the correct components whenever your data changes. Declarative views make it easier to understand and debug your code. Create encapsulated components that can manage their own state and then combine them to create complex UIs. JavaScript is used instead of templates to write component logic. This allows you to easily pass rich data through your app while keeping state out of the DOM. React doesn't make assumptions about your technology stack. This allows you to develop new features in React without having to rewrite existing code. React components implement a render() function that takes input data and returns what it should display. This example uses JSX syntax, which is a XML-like syntax. This.props allows you to access the input data passed into the component by calling render().

The RAPIDS software library, which is built on CUDAX AI, allows you to run end-to-end data science pipelines and analytics entirely on GPUs. It uses NVIDIA®, CUDA®, primitives for low level compute optimization. However, it exposes GPU parallelism through Python interfaces and high-bandwidth memories speed through user-friendly Python interfaces. RAPIDS also focuses its attention on data preparation tasks that are common for data science and analytics. This includes a familiar DataFrame API, which integrates with a variety machine learning algorithms for pipeline accelerations without having to pay serialization fees. RAPIDS supports multi-node, multiple-GPU deployments. This allows for greatly accelerated processing and training with larger datasets. You can accelerate your Python data science toolchain by making minimal code changes and learning no new tools. Machine learning models can be improved by being more accurate and deploying them faster.

With a single keystroke, switch between editor and command modes. Use the arrow keys to navigate between cells. All the Jupyter shortcuts are available. Fully interactive outputs are available right under the cell. Editing code cells is easy with smart code completion, quick error checking and quick fixes, and easy navigation. You can connect to remote JupyterHub or JupyterLab servers from the IDE. Interactively run Python scripts and arbitrary expressions in a Python Console. You can see the outputs and the state variables in real time. Split Python scripts into code cells using the #%% separator, and run them individually in a Jupyter notebook. Interactive controls allow you to browse DataFrames or visualizations in real time. All popular Python scientific libraries, including Plotly and Altair, ipywidgets and others, are supported.

Data scientists are able to build, improve, or operate end-to–end workflows independently. This allows them to deliver data science projects that are successful. Metaflow can be used with your favorite data science libraries such as SciKit Learn or Tensorflow. You can write your models in idiomatic Python codes with little to no learning. Metaflow also supports R language. Metaflow allows you to design your workflow, scale it, and then deploy it to production. It automatically tracks and versions all your data and experiments. It allows you to easily inspect the results in notebooks. Metaflow comes pre-installed with the tutorials so it's easy to get started. Metaflow allows you to make duplicates of all tutorials in your current directory by using the command line interface.

DSVMs are Azure Virtual Machine Images that have been pre-configured, configured, and tested with many popular tools that are used for data analytics and machine learning. A consistent setup across the team promotes collaboration, Azure scale, management, Near-Zero Setup and full cloud-based desktop to support data science. For one to three classroom scenarios or online courses, it is easy and quick to set up. Analytics can be run on all Azure hardware configurations, with both vertical and horizontal scaling. Only pay for what you use and when you use it. Pre-configured Deep Learning tools are readily available in GPU clusters. To make it easy to get started with the various tools and capabilities, such as Neural Networks (PYTorch and Tensorflow), templates and examples are available on the VMs. ), Data Wrangling (R, Python, Julia and SQL Server).

Hex combines the best of notebooks and BI into a seamless, collaborative interface. Hex is a modern Data Workspace. It makes it easy for you to connect to data and analyze it in collaborative SQL or Python-powered notebooks. You can also share work as interactive data apps or stories. The Projects page is your default landing page in Hex. You can quickly find the projects you have created and those you share with others. The outline gives you an easy-to-read overview of all cells in a project's Logic View. Each cell in the outline lists all variables it defines and any cells that return an output (chart cells or Input Parameters cells, etc.). Display a preview of the output. To jump to a specific position in the logic, you can click on any cell in the outline.

Bottle is a lightweight, fast and simple WSGI micro-web framework for Python. It is distributed in a single module file and does not have any dependencies except for the Python Standard Library. Support for clean and dynamic URLs. Requests to function call mapping. Built-in template engine that is fast and pythonic, and supports mako2, jinja2 or cheetah. Access to form data, file uploads cookies, headers, and other HTTP metadata. Built-in HTTP Development Server and support for paste bjoern gae cherrypy or other WSGI capable HTTP servers.

Falcon is a minimalist Python web API Framework for building robust microservices and app backends. The framework is compatible with both ASGI and gevent/meinheld. The Falcon web framework promotes REST architecture. Resource classes implement HTTP handlers to resolve requests and perform transitions. Falcon is a complement to more general Python webframeworks by adding extra reliability, flexibility and performance where you need it. You can use a number of Falcon templates, add-ons and complementary packages in your projects. You can find a few of these on the Falcon Wiki as a start, but you might also want to search PyPI for more resources.

Saturn Cloud is a data science and machine learning platform flexible enough for any team supporting Python, R, and more. Scale, collaborate, and utilize built-in management capabilities to aid you when you run your code.

All work can be synced, searched and managed across your data science team. Zepl's powerful search allows you to discover and reuse models, code, and other data. Zepl's enterprise collaboration platform allows you to query data from Snowflake or Athena and then build your models in Python. For enhanced interactions with your data, use dynamic forms and pivoting. Zepl creates new containers every time you open your notebook. This ensures that you have the same image each time your models are run. You can invite your team members to join you in a shared space, and they will be able to work together in real-time. Or they can simply leave comments on a notebook. You can share your work with fine-grained access controls. You can allow others to read, edit, run, and share your work. This will facilitate collaboration and distribution. All notebooks can be saved and versioned automatically. An easy-to-use interface allows you to name, manage, roll back, and roll back all versions. You can also export seamlessly into Github.

Alpine is a simple tool that allows you to create behavior in your markup. It's like jQuery for modern web. Add a script tag to get started. Declare an Alpine component and its data to a block of HTML. Dynamically assign HTML attributes to an element. Alpine cannot initialize a block of HTML. After Alpine has finished initializing the HTML, hide a block of HTML. Use the magic property to directly reference elements by their keys. Execute a script every time one of its dependencies changes. Alpine initializes an element and runs code.

Modular architecture allows for true flexibility and allows you to use any other libraries. A flexible ecosystem that can be used as a backbone for all types of server-side applications. Uses the latest JavaScript features to bring design patterns and mature solutions into Node.js. A complete development kit for building scalable server-side apps. JavaScript has been the "lingua franca of the web" for both front-end and backend applications in recent years thanks to Node.js. This has led to amazing projects such as Angular, React, and Vue that improve developer productivity and allow the creation of fast, extensible, and testable frontend applications. There are many great libraries, tools, and helpers for Node (and JavaScript server-side), but none of them solve the main problem of Architecture.

A powerful, extensible, and feature-packed frontend software toolkit. You can customize and build with Sass. Bootstrap is easy to use. You can either use the CDN, download the source code, or install it via the package manger. Bootstrap uses Sass to allow for modular and custom architecture. You can import only the components that you need, set global options such as gradients and shadows, and create your own CSS using our variables, maps and functions. You can import one stylesheet to get all the features of our CSS. You can customize Bootstrap by only including the CSS you require. Bootstrap 5 is constantly improving to make it easier to use CSS variables for global themes, individual components, or utilities. There are many variables that can be used to change colors, font styles, or any other aspect of Bootstrap 5.

Svelte Native is a mobile app framework powered by Svelte. It allows you to build mobile apps using the familiar web framework. You can create cross-platform native iOS and Android apps with no web views. Native UI and performance are possible while sharing code and skills with the web. Svelte's full power, including transitions, stores and reactivity, is yours. The mobile development experience is one of the most seamless. Svelte Native uses NativeScript to build mobile apps. NativeScript-Vue and React Native, both JavaScript mobile development frameworks, do most of their work on the device. Svelte Native shifts that work to a compile step when you build your application. Svelte uses techniques such as virtual DOM diffing to update the native view widgets whenever your app's state changes.

All the steps involved in building machine learning algorithms and predicting results, all in one click. Data Dialog allows you to easily shape your data without having to wrangle your files. Your prediction reports can be shared with your team members or made public. Let anyone make predictions on your model. Our low-code API allows you to integrate dynamic ML predictions directly into your app. Real-time prediction of willingness to pay, score leads, and many other things. AI gives you access to the most advanced algorithms in the world, without compromising on performance. Forecast revenue, optimize supply chain, personalize your marketing. Now you can see what the next steps are. In minutes, you can add a CSV file or integrate with your favorite data sources. Select your prediction column from the dropdown and we'll automatically build the AI. Visualize the top drivers, predicted results, and simulate "what-if?" scenarios.

Learn how to create Angular applications and reuse your code and skills to create apps for any deployment target. For web, mobile web and native mobile. Get the Web Platform running at its best today. You can also take it further with Web Workers and server side rendering. Angular gives you control over scaling. Build data models using RxJS, Immutable.js, or another push-model to meet your data needs. Simple, declarative templates make it easy to quickly build features. You can extend the template language by adding your own components or using a variety of existing components. Nearly every editor and IDE offers Angular-specific support and feedback. This allows you to focus on building amazing apps, not worrying about how the code works. Angular supports the largest Google applications, from prototype to global deployment.

Datalore is a platform for collaborative data science and analytics that aims to improve the entire analytics workflow and make working with data more enjoyable for both data scientists as well as data-savvy business teams. Datalore is a collaborative platform that focuses on data teams workflow. It offers technical-savvy business users the opportunity to work with data teams using no-code and low-code, as well as the power of Jupyter Notebooks. Datalore allows business users to perform analytic self-service. They can work with data using SQL or no-code cells, create reports, and dive deep into data. It allows core data teams to focus on simpler tasks. Datalore allows data scientists and analysts to share their results with ML Engineers. You can share your code with ML Engineers on powerful CPUs and GPUs, and you can collaborate with your colleagues in real time.

Dash & Dash Enterprise allow you to build and deploy analytic web applications using Python, R, or Julia. No JavaScript or DevOps are required. The world's most successful companies offer AI, ML and Python analytics at a fraction of the cost of full-stack development. Dash is the way they do it. Apps and dashboards that run advanced analytics such as NLP, forecasting and computer vision can be delivered. You can work in Python, R, or Julia. Reduce costs by migrating legacy per-seat license software to Dash Enterprise's unlimited end-user pricing model. You can deploy and update Dash apps faster without an IT or DevOps staff. You can create pixel-perfect web apps and dashboards without having to write any CSS. Kubernetes makes it easy to scale. High availability support for mission-critical Python apps

Core ML creates a model by applying a machine-learning algorithm to a collection of training data. A model is used to make predictions using new input data. Models can perform a variety of tasks which would be difficult to code or impractical. You can train a model, for example, to categorize images or detect specific objects in a photo based on its pixels. After creating the model, you can integrate it into your app and deploy on the device of the user. Your app uses Core ML and user data to make forecasts and train or fine-tune a model. Create ML, which is bundled with Xcode, allows you to build and train a ML model. Create ML models are Core ML formatted and ready to be used in your app. Core ML Tools can be used to convert models from other machine learning libraries into Core ML format. Core ML can be used to retrain a model on the device of a user.

NVIDIA Merlin enables data scientists, machine-learning engineers, and researchers, to build high-performance recommenders at scale. Merlin includes libraries, methods and tools to streamline the building and deployment of recommenders. These include addressing common challenges in preprocessing, feature engineering and training. Merlin components and capabilities have been optimized to support retrieval, scoring, filtering and ordering of hundreds terabytes data. All of this is accessible via easy-to-use interfaces. Merlin can help you make better predictions, increase click-through rates and deploy faster to production. NVIDIA Merlin is part of NVIDIA AI and advances our commitment to support innovative practitioners doing their best. NVIDIA Merlin is designed as an end-toend solution that can be integrated into existing recommender workflows utilizing data science and machine learning.

FastAPI is an advanced web framework that allows you to build APIs using Python 3.7+. It uses standard Python type hints. Fast: Very high-performance, on par with NodeJS or Go (thanks Pydantic and Starlette). One of the fastest Python Frameworks available. Reduce code duplication and get multiple features out of each parameter declaration.

Quadratic allows your team to collaborate on data analysis and deliver faster results. You're already familiar with spreadsheets, but this is the first time you have had so much power. Quadratic is fluent in Formulas, Python and SQL (JavaScript & SQL coming soon). Use the language that you and your team are already familiar with. Single-line formulas can be difficult to read. Quadratic allows you to expand your recipes as many times as you want. Quadratic comes with Python library support. Bring the latest open source tools to your spreadsheet. The last line of the code is returned to your spreadsheet. By default, raw values, 1/2D arrays and Pandas DataFrames can be used. Quadratic updates its cells automatically when data is pulled or fetched from an external API. Zoom out to see the big picture and zoom in for the details. Arrange your data and navigate it the way you see it in your mind, not as a tool would have it.

Many Python frameworks are capable of handling simple dashboards, but struggle with complex ones. This often leads to teams splitting into frontend and rearend roles, which can cause various challenges. Solara is a web framework that combines ReactJS with Python simplicity. It has a flexible API that allows for UI complexity and efficient state management. Solara is compatible with a variety of platforms, including Jupyter and Voila. It also supports a wide range of applications from dashboards to prototypes. It focuses on code quality, developer access, and robust testing.

esDynamic allows you to maximize your security testing journey. From setting up your bench, to analyzing the results of your data processing, esDynamic will save you time and effort. Discover the comprehensive and flexible Python-based platform that is perfect for every phase of security analysis. You can easily customize your research space by adding new equipment, integrating different tools, and changing data. esDynamic also offers a large collection of materials that cover complex topics, which would normally require extensive research and a team of experts. This gives you instant access to expertise. Say goodbye to fragmented data and scattered knowledge. Welcome to a cohesive workspace that allows your team to easily share data and insights. This will foster collaboration and accelerate discoveries. Share your JupyterLab Notebooks with your team to centralize and solidify all your efforts.

SAS®, Viya®, data science offerings offer a comprehensive, scalable analytical environment that is quick and easy to use, allowing you to meet diverse business requirements. Automatically generated insights allow you to identify the most commonly used variables across all models, the most significant variables selected across models, and assess results for all models. Natural language generation capabilities allow you to create project summaries in plain language. This makes it easy to interpret reports. Analytics team members can add project notes and comments to the insights report to facilitate communication between team members. SAS allows you to embed open source code into an analysis and call open-source algorithms seamlessly within its environment. This allows for collaboration within your organization as users can program in the language they prefer. SAS Deep Learning with Python (DLPy) is also available on GitHub.

Find data insights that will help you solve business and research problems. IBM®, SPSS®, Statistics is a powerful statistical platform. It features a user-friendly interface, a robust set of capabilities that allow your organization to quickly extract actionable insights out of your data. Advanced statistical techniques ensure high quality and accuracy in decision making. All aspects of the analytics lifecycle, from data preparation and management to analysis, reporting and reporting, are covered. An intuitive user interface makes it easy to prepare and analyze data without writing code. You can enhance SPSS syntax with R or Python by using a variety of extensions or building your own. An integrated interface allows you to run advanced and descriptive statistics, regression analysis and decision trees. You can choose from traditional or subscription licenses with multiple capabilities depending on your needs.

IBM Spectrum®, Discover is a modern software for metadata management that provides data insights on petabytes of unstructured storage. The software connects easily to IBM Cloud™, Object Storage, and IBM Spectrum Scale, allowing it to quickly ingest, consolidate and index metadata for millions of files and objects. IBM Spectrum Discover is a rich metadata layer which enables storage administrators, data managers, and data scientists, to efficiently manage, categorize, and gain insights from massive unstructured amounts of data. It helps reduce risk, improves storage economics and accelerates large scale analytics to create a competitive advantage and speed up critical research. Unstructured data management is a major challenge for many companies. Unstructured data is information that does not have a data model or is organized in a defined manner. Some of the challenges that companies face are:

With a fully automated cloud infrastructure, experts can explore data and write stable codes at the same time. Zerve’s data science environment gives data scientists and ML teams a unified workspace to explore, collaborate and build data science & AI project like never before. Zerve provides true language interoperability. Users can use Python, R SQL or Markdown in the same canvas and connect these code blocks. Zerve offers unlimited parallelization, allowing for code blocks and containers to run in parallel at any stage of development. Analysis artifacts can be automatically serialized, stored and preserved. This allows you to change a step without having to rerun previous steps. Selecting compute resources and memory in a fine-grained manner for complex data transformation.

Dask is free and open-source. It was developed in collaboration with other community projects such as NumPy and pandas. Dask uses existing Python data structures and APIs to make it easy for users to switch between NumPy/pandas and scikit-learn-powered versions. Dask's schedulers can scale to thousands of node clusters, and its algorithms have been tested at some of the most powerful supercomputers around the world. You don't necessarily need a large cluster to get started. Dask ships schedulers that can be used on personal computers. Many people use Dask to scale computations on their laptops, using multiple cores and their disk for extra storage. Dask exposes lower level APIs that allow you to build custom systems for your own applications. This allows open-source leaders to parallelize their own packages, and business leaders to scale custom business logic.

You can quickly find answers with the flexible visual query builder. You can access data via your browser or phone and analyze it anywhere you are. No SQL, data scientist, or desktop required. You can learn from your team mates and search for any questions within your organization with shared questions. @mentions, push notifications and shared chat allow you to bring the right people in the conversation and make data actionable. You can easily copy and modify shared questions, export data and change the way charts are displayed so you don't just see someone else's analysis but make it yours. External sharing can be turned on to allow access to data tables and partners outside your domain. In just two clicks, you can access the underlying data tables. Smart typeaheads make it easy to run custom SQL.

Growler is a web-framework built on top of asyncio. This asynchronous library was described in PEP 3156 and added to python's standard library with version 3.4. It takes its cues from the Connect & express frameworks within the nodejs eco-system, which uses a single application object with a series of middleware for processing HTTP requests. The custom middleware chain is a simple way to implement complex apps. The pip utility lets packages specify optional requirements so that features can be installed only on request. This fits well with the minimal approach of the Growler project. Don't install anything that the user doesn’t need. There are (will) be community packages that have been approved by the Growler developers. (After ensuring that they work as expected and have been well tested with each growler version) These will be available directly from the Growler package.

Data science platform that increases productivity and has unparalleled capabilities. Create and evaluate machine learning (ML), models of higher quality. Easy deployment of ML models can help increase business flexibility and enable enterprise-trusted data work faster. Cloud-based platforms can be used to uncover new business insights. Iterative processes are necessary to build a machine-learning model. This ebook will explain how machine learning models are constructed and break down the process. Use notebooks to build and test machine learning algorithms. AutoML will show you the results of data science. It is easier and faster to create high-quality models. Automated machine-learning capabilities quickly analyze the data and recommend the best data features and algorithms. Automated machine learning also tunes the model and explains its results.

Backbone.js provides structure for web applications by providing models with key/value binding and custom events. It also provides collections with rich APIs of enumerable function, views with declarative handling and views with declarative event handling. All this is connected to your existing API via a RESTful JSON interface. One of the most important things to do when working on a web app that uses a lot JavaScript is to stop tying data to the DOM. It is easy to create JavaScript apps that are tangled with jQuery selectors or callbacks. They all try to keep the data in sync between your HTML UI, JavaScript logic, and your database. A more structured approach is often better for rich client-side apps. Backbone lets you represent your data in Models. These Models can be created, validated and destroyed, and saved to the server.

Bitfount provides a platform for distributed data sciences. We enable deep data collaborations that do not require data sharing. Distributed data science connects algorithms to data and not the other way around. In minutes, you can set up a federated privacy protecting analytics and machine learning network. This will allow your team to focus on innovation and insights instead of bureaucracy. Although your data team is equipped with the skills to solve your most difficult problems and innovating, they are hindered by data access barriers. Are you having trouble accessing your data? Are compliance processes taking too much time? Bitfount offers a better way for data experts to be unleashed. Connect siloed or multi-cloud data sources while protecting privacy and commercial sensitivity. No expensive, time-consuming data lift-and-shift. Useage-based access control to ensure that teams only do the analysis you need, with the data you want. Transfer access control management to the teams that have the data.

Brilent is a data-science tech company that develops a SaaS solution for talent seekers to quickly and effectively find the right talent to hire. This intelligent technology is simple, which is the best part. There are no tricks. It uses the components that recruiters find most relevant. The core elements of the Brilent engine include three simple elements: job requirements, candidate profile, and our unique repository market data. The fun part is next. Our system collects all relevant data from job requirements and candidate profiles. We use hundreds of variables from the market data and familiar elements in recruiting to make predictions about whether a candidate will be a good match for a job. This is a lot of data crunching done in seconds. Recruiters can then rank candidates according to their specifications.

Data is essential when it comes to constructing, streamlining and growing your company. Unfortunately, it can be difficult to get the most out of data. Many organizations face incompatibilities, slow results, poor access to data and spiraling costs. Leaders who can transform raw data into real results are the ones who will succeed in today's competitive landscape. This is possible by empowering everyone in your company to be able analyze, build, and collaborate on machine learning and AI solutions. Streamflux is a one stop shop for all your data analytics and AI needs. Our self-service platform gives you the freedom to create end-to-end data solutions. It uses models to answer complex questions, and evaluates user behavior. You can transform raw data into real business impact in days instead of months, whether you are generating recommendations or predicting customer turnover and future revenue.

An end-to-end solution gives you all the tools your data science team needs to scale your machine learning development, from research to production. cnvrg.io, the world's leading data science platform for MLOps (model management) is a leader in creating cutting-edge machine-learning development solutions that allow you to build high-impact models in half the time. In a collaborative and clear machine learning management environment, bridge science and engineering teams. Use interactive workspaces, dashboards and model repositories to communicate and reproduce results. You should be less concerned about technical complexity and more focused on creating high-impact ML models. The Cnvrg.io container based infrastructure simplifies engineering heavy tasks such as tracking, monitoring and configuration, compute resource management, server infrastructure, feature extraction, model deployment, and serving infrastructure.