Plotly is based on Tioh’tia:ke, commonly known in English as Montreal Island, in Kanien’ke-há:ka, the “Place of the People of the Flint” (the Kanien’kéha).

Plotly is committed to making the tech industry more accessible to people from different cultures, as well as using and encouraging the use of our technology for anti-racist efforts. For our complete land acknowledgement and resources to learn about settlement and contribute to Indigenous activism, visit our About Us page.

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Dash Enterprise is productionizing Python data apps in every industry.

DASH ENTERPRISE

Industry App Catalog

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Access these pre-built Dash apps by industry as inspiration and starter templates.

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Dash helps data scientists & quants operationalize Python models

Global Pharmaceutical Company & Dash Enterprise Customer

"The distinguishing factor between PowerBI and what Dash NLP analytic apps can do, is offer that next step up of a 'cognitive dashboard' rather than all the other standard dashboards out there."

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Dash is the fastest way to deploy Python-based apps for natural language processing (NLP).

Use NLP + Dash to extract business value from large amounts of unstructured text:

This Dash app demos Google's word2vec in ~300 lines of Python code

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Dash is the fastest way to deploy Python-based apps for computer vision.

Use computer vision + Dash for object detection in:

This Dash app demos DETR object detection in ~200 lines of Python code

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Dash is the fastest way to deploy Python-based apps for dimensionality reduction.

This Dash app demos TSNE clustering in ~300 lines of Python code

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Dash is the fastest way to deploy Python-based apps for predictive analytics and forecasting.

Use predictive analytics + Dash to put neural networks, nonlinear regressions, decision trees, SVMs, and other forecasting methods in the hands of business users.

Dash operationalizes predictive models for:

This Dash app demos Facebook’s Prophet library in ~200 lines of Python code

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Dash is the fastest way to deploy Python-based apps for voice computing.

This Dash app demos speech recognition in ~100 lines of Python code

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Deploy Flask APIs to Dash Enterprise that load trained models, accept feature values in POST requests, and respond with predicted values.

Access this Dash app and get the Python code.

This Dash app loads scenes collected from self-driving car trips, and displays both lidar sensor data and human annotated data (bounding boxes). You can choose between a basic and an advanced UI. Get the source code and read the blog post.

This Dash app shows how to build a real-time AV explorer from scratch using Dash Deck and the Lyft perception dataset. The code is easily customizable and also compatible with 3D detection models written in Python.

Given a pre-annotated video, this Dash app lets you run 2D object detection on a selected frame. You can also edit the annotations, add new bounding boxes, and export the new data table.

The release of DETR: End-to-End Object Detection with Transformers showed significant improvement in real-time object detection and panoptic segmentation (PS), while greatly simplifying the architecture. As a mean to test the model, we decided to build a simple Dash app that let you experiment and play with the model through a user interface.

This Dash app shows how Dash can be used to execute AI models like the YOLO v3 object-detection algorithm in real-time. Check out this article for full details.

This Dash app analyzes 120 million Uber trips in real-time to predict the cost of Uber trips through NYC. Read the blog post.

This Dash app displays well data from New York State. As you hover over values in the map, a time series is displayed showing production values over time. As you change the years in the range slider, the aggregate time series is updated with the sum of all production over time. The histogram chart is also selectable, serving as an alternative control for selecting a range of time.

This Dash app creates a dashboard for mineral composition evaluations from natural gas wells. By pairing geographic map of well locations with ternary diagram, this app helps geologists to find out results of gas wells with relationship to formations or rock types.

This Dash app takes a Log ASCII Standard (LAS) file as input and builds a web app to visualize drilling data.

This Dash app was published in Nature Energy by the UC Berkeley Energy Resource Group. It calculates the levelized cost of electricity (LCoE) for off-grid solar deployments in Sub-Saharan Africa.
Read the UC Berkeley announcement.

This peak load forecasting Dash app provides two helpful predictions for New York City building owners, facility managers, and residents interested in tomorrow's total electricity grid demand. The first model is a next-day hourly load curve for the NYISO. The second is a confidence interval representing the percent chance that tomorrow will be a peak load event. The Python model behind the Dash app was framed as a supervised learning problem in order to compare the performance of a Regression Tree, Random Forest Regressor, and XG Boost algorithms.

This Dash app uses pre-trained Decision Tree Classifiers to perform real-time predictions on tabular data (information about a loan applicant that was recorded by a lending firm). To read the model data, this Dash app connects to a Snowflake data warehouse.

This Dash lets you train and evaluate a L2 regression (Ridge) model for predicting the interest rates of loans based on various factors. It lets you see how well a model performs (actual vs predicted plot), as well as compare the importance of these features. To read the model data, this Dash app connects to a Snowflake data warehouse.

This Dash app performs natural language processing on a database of consumer complaints towards US financial institutions. Compare consumer complaint sentiments between Wells Fargo, Citibank, Equifax, JP Morgan and dozens of other institutions.

This Dash app uses Facebook's Prophet time series forecasting model to perform predictive analysis of regional sales. The data was collected for locations across various states and for various market segments, and the model was used to forecast future sales, which is useful for making informed decision and infer the supply needed for future months.

This Dash app simulates the efficient frontier of a set of portfolios based on the Monte Carlo method, then generates a 16 page report of the result.

This Dash app demonstrates the use of Design Kit and Snapshot Engine for generating enterprise-grade, interactive reports with Python and Dash.

This Dash app demonstrates the use of Design Kit and Snapshot Engine for triggering alerts and automatic report generation for out-of-spec process parameters.

This object-detection Dash app provides real-time visualizations about what's happening inside a complex video.

This Dash app monitors SKU product availability by product, supplier, and store region. It also allows stores to track daily distribution and future inventory predictions.

This statistical process control (SPC) Dash app simulates monitoring real-time process quality along a manufacture production line.

This Dash app allows users to annotate medical image datasets of COVID-19 patients in order to extract a lung occlusion (and train machine learning models).

This Dash app uses machine learning in order to compute the segmentation of an image, given user-provided annotations. In addition to Plotly and Dash, the app uses off-the-shelf algorithms and estimators from PyData packages, namely scikit-image and scikit-learn.

This Dash app segments an image into regions with scikit-image and computes a number of properties for these regions such as their area, their perimeter length, etc. Each region is then annotated on the image and entered in a Dash DataTable. The annotated image and the DataTable are dynamically linked together, so that interacting with one changes the displayed information in the other

This app allows you to:
Interactively move through sliced representations of the brain
See an overlay of the automatically computed over-segmentation of the brain into superpixels (regions of the brain)
Highlight one or several superpixels
See the highlighted superpixels rendered in a 3D glass brain representation
Download a file with that contains the highlighted superpixels 

Access this Dash app and get the Python code. 

To ensure adequate inventory, retailers need to continuously monitor levels of stock. Using Prophet, a procedure for forecasting time series data, this Dash app is able to process and interactively display availability levels by category, region, supplier, and product. Inventory managers can respond to the daily distribution levels and plan future inventory.

Monitoring competitors’ websites is critical to determine the best product pricing and positioning. This app uses Scrapy, a Python web-crawling framework, to mine data from multiple websites for top-rated products, price changes, and website position. Dash easily displays this information and empowers companies to optimize their market position in real-time.

The consumer decision-making process is important for retailers to understand. What will be the second choice if the first item is out of stock? This app visualizes demand transference predictions based on transactional log data, store attributes, and external sources. It adjusts for pricing, stock, and seasonality to help retailers determine and adjust to the relative level of demand of items.

Revenue protection is crucial in industries that may have fluctuating yield levels. This Dash app presents a fully interactive Sankey diagram that enables the user to view crossovers between vulnerable product ingredients, growing regions, presentation form, and consumption sites. With this information, businesses can quickly identify the percentage of predicted revenue that is protected versus unprotected before production.

Predict customer and revenue churn through survival analysis models. 

https://plotly.com/dash/industry-app-catalog/

Dash Enterprise is the fastest way to deploy front-ends for machine learning back-ends with dozens of easily customizable ML and AI templates.

Access a curated selection of user stories, white papers, and how-to guides from data science experts and industry leaders. Start your data app journey today.

Data science and analytics best practices, trends, success stories, and expert-curated tutorials for modern data teams and leaders.

Plotly Professional Services are experts in delivering future-proof data apps. From custom development to workshops, they can support your analytics needs.

Consulting, Training & Open-Source Development

Combined with Python, Plotly Dash delivers interactive, customizable data apps. Explore examples in a wide range of industries and advanced analytic needs.

Plotly Customer Success empowers Dash Enterprise customers to drive insight and decision-making with data. The award-winning team offers support, enablement sessions, technical account management, and more.

Dash is a framework for building data apps in Python. Dash Enterprise simplifies the development and deployment process in a secure, scalable environment.

Plotly: The front end for ML and data science models

Find answers to common questions you may have about Dash Enterprise, including migration, installation, deployment, and licensing.

Headquartered in Montreal with a remote-first team, Plotly is dedicated to ensuring that every organization uses Dash Enterprise to build data applications.

As a category-defining leader in enabling data-driven decisions, Plotly is frequently featured in the news. Explore recent articles, podcasts, and blog posts.

Deploy front-ends for machine learning back-ends like PyTorch, Keras, and TensorFlow with custom Life Sciences templates in Dash Enterprise.

Compare Dash Enterprise, RStudio Connect, Anaconda Enterprise, and Streamlit for Teams as deployment platforms. See differences in authentication and licensing.

Dash Enterprise vs RStudio, Anaconda, Streamlit

Dash is the solution for operationalizing your AI/ML initiatives, with GPT-3, Hugging Face Transformers, UMAP, and YOLOv3. Check out latest Dash-AI projects.

Learn how Dash, Posit (Shiny), and Streamlit compare as low-code UI layers for data apps. See the differences in architecture, deployment, user experience, and more.

How do Dash, Posit (Shiny), and Streamlit compare?

Have a question about Plotly or want to know how to get started with Dash Enterprise? Fill out this form, and a member of the our team will get in touch soon.

Compare Dash Enterprise, Tableau, Power BI, and Einstein Analytics. See the differences in deployment process, ease of use, security, support, and more.

Dash Enterprise for Python vs Common BI Software

Learn how low-code UI layers like Dash, Posit (Shiny), Streamlit, and Bokeh compare in web protocol, architecture, user experience, licensing, deployment, and more.

How do Dash, Shiny, Streamlit, and Bokeh compare?

Compare the differences between Dash Open Source and Dash Enterprise. Start deploying unlimited Dash apps for unlimited end-users. Request a custom price-quote.

Plotly's company culture focuses on autonomy, trust, and data. Join the remote-first team pioneering analytic innovation and leading the data app industry.

Have a question about Plotly or want to know how to get started with Dash? Share feedback in this form, and a member of the our team will get in touch soon.

Go from data exploration to actionable insight faster. Schedule a demo to learn how Dash Enterprise enables powerful, customizable, interactive data apps.

Please see more details on Plotly's privacy policy, including information collected, security, retention, international visitors, and third-party services.

Use Plotly’s Dash web app framework with Databricks (Python SQL connector, Jobs API,  CLI, databricks-connect-v2) for at-scale production data apps.

Python is one of the best tools for data scientists in analyzing & developing models. Here are some resources to help you say goodbye to Excel, and hello to Dash.

As a Python-based framework, Dash offers integration with ChatGPT and large language models (LLMs). Learn how you can take part in the future of AI-enabled apps.

After installing Dash Enterprise in one of the below environments, you will be asked for a license key to activate the software and begin deploying Dash apps.

Learn how Dash Enterprise installs via automated scripts and get started with Dash Enterprise on Google Kubernetes Engine (GKE).

Learn how to adapt your Dash app with Dask to use Coiled in the back end. Using the integration can enable scaling across parallel computing clusters.

Dash Enterprise seamlessly integrates CI/CD for streamlined app deployment. Git-based workflow, automated QA, version control, and more included.

Dash Enterprise supports turnkey connections to popular backends in Python – Vaex, Dask, Datashader, RAPIDS, Databricks (PySpark), Snowflake, and Postgres.

Workspaces is an onboard code editor from Dash Enterprise. Easily write and execute code for data apps. No additional downloads or installation needed.

With Persistent Filesystems, simplify your data science workflow and avoid network delays. Large data can be uploaded, updated, and cached in Dash Enterprise.

Connect Dash directly to Snowfake, a data warehouse offered by Amazon Web Services. Dash offers an ideal frontend to interact with real-time AI/ML processes.

Dash Enterprise installs via automated scripts. Get started with Dash Enterprise on Microsoft Azure Kubernetes Service (AKS).

Deploy, manage, and share data apps. | With the App Manager from Dash Enterprise, ensure a standard, well-documented, and reproducible workflow.

Easily style and customize data apps with Design Kit, a low-code framework from Dash Enterprise. Production ready — no JavaScript, HTML, or CSS required.

Dash Enterprise installs via automated scripts. Get started with Dash Enterprise on Amazon Elastic Kubernetes Service (Amazon EKS).

Provide data app end users with a powerful, drag-and-drop UI. Dashboard Engine from Dash Enterprise is Python ETL meeds BI — no callbacks or layout required.

Dash Enterprise Job Queue makes background jobs seamless and scalable in Python. Easily offload CPU-intensive tasks and improve data app performance.

Dash Enterprise Embedding Middleware enables seamless embedding without the use of iframes. It also offers single sign-on for easy authentication that scales.

Create, annotate, archive, and share views of your Dash apps. With only a few lines of code, Snapshot Engine schedules tasks and auto-generates reports.

Access a GUI for easy and secure authentication. With App Manager from Dash Enterprise, choose from LDAP, AD, PingFederate, Okta, SAML, SSO, and more.

Leverage Plotly's library of recorded webinars to learn data analytics tips, industry best practices, and application use cases from data analytics experts.

Learn how Université de Sherbrooke uses Plotly to develop new analytical tools to explore and visualize genetic structures at the school's CoBIUS Lab.

Plotly & Université de Sherbrooke to develop genomic visualization

Plotly receives the Canadian SME award for Top Place to Work and Business of the Year for driving product innovation and employee professional development.

Canadian SME National Business Awards selects Plotly

Dash DAQ provides Python users the means to create interactive virtual control panels on their computers that interface with common lab instruments.

Dash Enterprise 5.0 is built on Kubernetes architecture, providing low-code capabilities for data scientists and drag-and-drop custom views for business users.

Explore Python applications for manufacturing and research and development, ranging from laboratory data collection, IoT, real-time dashboards, and more.

Explore examples of Python in bioinformatics in a variety of applications, ranging from genomics, public health, clinical trials, 3D images, and more.

Bioinformatics & Life Sciences Dash App Examples

Thanks to the flexibility of Python, Dash applications can connect to any API. Explore data applications that connect to external APIs and visualize data.

Explore examples of Python and Dash in finance ranging from quantitative analysis, machine learning, portfolio optimization, and NLP.

Explore Dash applications that incorporate ChatGPT and generative AI in Python.

ChatGPT & Generative AI Dash App Examples

Data visualization and dashboards is what Plotly and Dash is known for. Explore several of these Python applications for data visualization and dashboards.

Data Visualization & Dashboards Dash App Examples

Explore Python applications for science and engineering, including finite element method, contract for difference, physics modeling, and simulations, and more.

Interested in Plotly and Dash for geospatial projects and use cases? Explore these Dash data applications that take advantage of the flexibility of Python.

Explore Python and Dash applications for machine learning projects, ranging in a variety of use cases from SHAP explainability, segmentation, and more.

Explore detailed and practical examples of Python in predictive analytics and forecasting to go beyond common BI limitations.

Explore Dash data applications that run interactive image processing, computer vision, video analysis, and audio engineering with the power of Python.

Explore Python and data science Dash applications for business-related use cases, ranging from customer segmentation, retail demand forecasting, and more.

Explore examples of Python and data science applications in the energy and utilities industry, including electricity, oil and gas, renewables, and more.

Explore Python data applications for natural language processing (NLP). The following Plotly and Dash applications examples will speak for themselves.

Data analytics and data scientists meet sports fans with Dash. Explore Dash data applications that convey sports analytics using the flexibility of Python.

Learn how Plotly prioritizes data security as the premier platform for developing, scaling, and deploying Python-based data apps.

By leveraging these best practices and the provided code, you can harness the power of Sankey Diagrams to gain insights from data. Learn more in this Plotly article.

Dive into the world of data science in finance, where analytics and insights reshape the future of financial decision-making and innovation.

Plotly is now SOC2 Type I and Type II compliant. Learn how this helps protect customer assets and data sources when using Plotly products.

Enhanced Security and Reliability: Plotly Is SOC 2 Type II Compliant

Learn how to accelerate data app creation using GitHub Copilot with Dash Open Source and Dash Enterprise Solutions, for streamlined development.

Dash + GitHub Copilot = Easier and Faster Data App Building

Discover the power of leveraging Polars in lightning-fast Dash apps for visualizing and analyzing large data sets in your production environment.

How Polars Can Help You Build Fast Dash Apps for Large Datasets

Automate and streamline digital marketing workflows with by building custom digital marketing data apps and dashboards using Advertools and Dash.

Automating and Streamlining Digital Marketing Workflows with Plotly and Dash

Easily build and deploy an interactive dashboard interface for your scikit-learn models directly on the web, with Plotly Dash.

Build Python Web Apps for scikit-learn Models with Plotly Dash

Python is one of the best tools for data scientists in analyzing & developing models. Here is a list of the top 15 python tools for data scientists.

Plotly Professional Services team shares how to effectively visualize geospatial datasets in data apps, specifically for vector, polygon data.

Performance Optimizations for Geospatial Dash Apps

Discover how Anna Riley, Plotly Professional Services team member and athlete, visualized her Strava data in just two weeks with Dash.

Reflections from an Athlete — Dash feat. Strava

In this blog post from Plotly Community Manager, Adam Schroeder, learn how to leverage Matplotlib's robust plots with Dash's intuitive API to create dynamic and immersive data visualizations.

Creating an Interactive Web App with Matplotlib, Python, and Dash

In this blog post from Plotly's CPO and the Creator of Dash, Chris Parmer, learn how Partial Property Updates offers substantial data app performance improvements.

Partial Property Updates: Extending Full-Stack Development with Python

Excel is a complicated tool with a lot of functions that aren’t always intuitive. We’ve got a few ways Python can improve Excel functions. Learn more in this article.

Generating Excel reports can be tedious & time-consuming, but there’s a solution. Automating your reports using Python. Learn more.

How to Automate Excel Reports Using Python | Plotly

Industry App Catalog

About Industry App Catalog

Dash helps data scientists & quants operationalize Python models

"The distinguishing factor between PowerBI and what Dash NLP analytic apps can do, is offer that next step up of a 'cognitive dashboard' rather than all the other standard dashboards out there."

NLP

AV Lidar Visualization

Lyft AV Explorer

Video Annotation

DETR Object Detection

Real-time Object Detection

Uber Price Prediction

Build. Deploy. Scale.