Special thanks to Plotly investor, NVIDIA, for their help in reviewing these open-source Dash applications for autonomous vehicle R&D, and Lyft for initial data visualization development in Plotly.

Author: Xing Han Lu, @xhlulu

📌 To learn more about how to use Dash for Autonomous Vehicle and AI Applications register for our recorded webinar with Xing Han Lu, Plotly’s Lead AI/ML Engineer

Imagine eating your lunch, streaming your favorite TV shows, or chatting with your friends through video call inside the comfort of your car as it manages to drive you from home to work, to an appointment, or to a…


📌 Want to learn how to use Dash for Julia to build high-performance dashboards? Check out our recorded webinar!

Today, we’re excited to officially welcome Dash for Julia. By adding Julia to Dash’s Python and R back ends, the Jupyter trinity is complete again.

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“Dash is a productive Python framework for building web applications,” so starts Plotly’s Dash documentation. That is indeed how Dash began, allowing data scientists to build rich, beautiful, exploratory portals to their data using only Python. But, describing Dash as a Python framework misses a key feature of its design: the Python side (the back end/server)…


📌 Learn how to deliver AI for Big Data using Dash & Databricks this recorded webinar with Peter Kim of Plotly and Prasad Kona of Databricks.

We’re delighted to announce that Plotly and Databricks are partnering to bring cloud-distributed Artificial Intelligence (AI) & Machine Learning (ML) to a vastly wider audience of business users. By integrating the Plotly Dash frontend with the Databricks backend, we are offering a seamless process to transform AI and ML models into production-ready, dynamic, interactive, web applications. This partnership with Databricks empowers Python developers to easily and quickly build Dash apps that are connected to…


In a pub located in downtown Montreal (the same city where Plotly was founded), some evening in 2014, then PhD student Ian Goodfellow thought about a game theory-inspired approach to generate realistic images using Deep Learning: what if we could pitch two neural networks to play a game, where one would generate images from random noise (the generator), and the other one (the discriminator) would learn to predict whether an image is real (drawn from some training set) or fake (generated by the adversary). If you train those models correctly, you would have a generator that is good enough to…


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AI-enhanced BI powered by Dash and Snowflake — (Check out the app here)

Business intelligence (BI) is an indispensable tool for many, if not most, modern organizations. BI covers an entire gamut of end-to-end activities from data mining to reporting, all carried out with a core goal assisting critical business decision making.

How significant has BI become? One indication of its popularity can be gleaned from this Google Trends chart showing its search popularity over the last five years.


We’re excited to announce the release of JupyterDash, our new library that makes it easy to build Dash apps from Jupyter environments (e.g. classic Notebook, JupyterLab, Visual Studio Code notebooks, nteract, PyCharm notebooks, etc.).

Dash is Plotly’s open source Python (and R and Julia!) framework for building full stack analytic web applications using pure Python (no JavaScript required). Thanks to features like hot reloading and front-end error reporting provided by Dash DevTools, developers can quickly iterate on application designs using a traditional text editor or Integrated Development Environment (IDE). JupyterDash makes these features, and more, available from the Jupyter notebook.

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We’re pleased to announce that Plotly and NVIDIA are partnering to bring GPU-accelerated Artificial Intelligence (AI) & Machine Learning (ML) to a vastly wider audience of business users. By integrating the Plotly Dash frontend with the NVIDIA RAPIDS backend, we are offering one of the highest performance AI & ML stacks available in Python today. This is all open-source and accessible in a few lines of Python code.

On the Enterprise side, Dash Enterprise Kubernetes (DEK)now ships with out-of-the-box support for horizontally scalable GPU acceleration through RAPIDS and Dask. Once you’ve created a Dash + RAPIDS app on your desktop…


Representing words in a numerical format has been a challenging and important first step in building any kind of Machine Learning (ML) system for processing natural language, be it for modelling social media sentiment, classifying emails, recognizing names inside documents, or translating sentences into other languages. Machine Learning models can take as input vectors and matrices, but they are unable to directly parse strings. Instead, you need to preprocess your documents, so they can be correctly fed into your ML models. Traditionally, methods like bag-of-words have been very effective in converting sentences or documents into fixed-size matrices. Although effective, they…


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A Todo app made with Dash’s Pattern-Matching Callbacks

Written by: Alex Johnson, Plotly CTO

Before we start, I’d like to thank codefour for generously sponsoring this open-source feature and providing excellent feedback throughout its development. Collaborating with talented teams like codefour is one of the most rewarding parts of my job as Plotly’s CTO. Cornerstone, open-source features like Pattern-Matching Callbacks will ultimately be used by 100’s of millions of Dash applications editors and viewers — so these collaborations are special. If you’d like to discuss your idea for accelerating Dash’s open-source roadmap, please get in touch.

Now let’s dive in.

Dash is a rich, customizable, and dynamic framework…


Self-driving cars are widely expected to be the future of personal transportation. Many companies, from startups to tech giants, have been investing in their R&D team in hopes of becoming the first to achieve full, Level 5 self-driving automation. One important feature that self-driving cars currently have is the ability to use Artificial Intelligence and Machine Learning to detect, localize, and categorize their surroundings — so they are able to make decisions in real-time based on the most accurate information. For this reason, the Udacity team released a dataset of driving scenes, where each object is annotated with a label…

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