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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Plotly Wins Bronze Award in the category of Achievement in the Use of Data & Analytics at the 2023 Stevie Awards for Sales & Customer Service.

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.

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."

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

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

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

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

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

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. 

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

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

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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.

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

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

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

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

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.

Dash is the fastest way to deploy front-ends for ML backends such as PyTorch, Keras, and TensorFlow. To accelerate Dash Bio adoption among businesses, Dash Enterprise ships with dozens of Life Science templates that can be easily customized for your own data.

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

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

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

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

Plotly is the easiest way to graph and share your data.

Plotly Open Source Graphing Libraries |  | Plotly

Learn how Dash, 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, Shiny, and Streamlit compare?

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

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

How do Dash Enterprise, RStudio Connect, Anaconda Enterprise, and Streamlit for Teams compare?

Dash apps go where Tableau and PowerBI cannot... NLP, object detection, predictive analytics, and more. With 0.5M+ downloads/month, Dash is the new standard for AI &amp; data science apps.

Dash Enterprise installs via automated scripts. 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.

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.

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.

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

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.

Dash Enterprise ships with turnkey support for horizontally scalable Dask clusters, putting Python's most popular HPC stack in the hands of business users.

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

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

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

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

The stateless design of Dash app servers enables horizontal scaling on modern infrastructure like Kubernetes with top clouds like AWS, Azure, and GCP.

Continuous integration (CI) and continuous deployment (CD) integrates naturally with Dash Enterprise's modern, git-based deployment workflow. Dash Enterprise ships with everything you need to make the most of your CI/CD platform for Dash app deployment: templates for automated QA, pre-production app review, git-based version control, and more.

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.

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

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

With Dash, access a library of AI/ML templates that work seamlessly with back ends like PyTorch, Keras, and TensorFlow. Easily operationalize Python models.

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.

Dash is the fastest way to deploy front-ends for ML backends such as PyTorch, Keras, and TensorFlow. To accelerate AI adoption among businesses, Dash Enterprise ships with dozens of ML &amp; AI templates that can be easily customized for your own data.

Use Plotly's Dash web app framework and Databricks (DB SQL, Jobs API, databricks-dash) to create at-scale, interactive, customizable data apps.

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.

Scale AI contributed a grant to Plotly in 2020 to speed innovation in supply chain AI for several Canadian companies, such as Hydro-Québec and Dex Clothing.

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.

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

Dash is the fastest way to deploy front-ends for ML backends such as PyTorch, Keras, and TensorFlow. To accelerate AI adoption among businesses, Dash Enterprise ships with dozens of ML & AI templates that can be easily customized for your own data.

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.