Carmen  Grimes

Carmen Grimes

1614070777

Gradio vs Streamlit vs Dash vs Flask

Comparing several web UI tools for data science!

Introduction

Machine learning models are exciting and powerful, but they aren’t very useful by themselves. Once a model is complete, it likely has to be deployed before it can deliver any sort of value. As well, being able to deploy a preliminary model or a prototype to get feedback from other stakeholders is extremely useful.

Recently, there has been an emergence of several tools that Data Scientists can use to quickly and easily deploy a machine learning model. In this article, we’re going to look at 4 alternatives that you can use to deploy a machine learning model: Gradio, Streamlit, Dash, and Flask.

Keep in mind that this is an opinionated article and is solely based off of my knowledge and experiences with these tools.

Summary: Which Should I Use?

Image for post

Gradio: Gradio is specifically built with machine learning models in mind. So if you want to create a web UI specifically for a machine learning model that you built, Gradio’s simple syntax and setup is the way to go.

Streamlit: Streamlit is useful if you want to get a dashboard up and running quickly, and have the flexibility to add lots of components and controls. As well, Streamlit allows you to build a web UI or a dashboard much faster than Dash or Flask.

Dash: Choose Dash if you want to be a production-ready dashboard for a larger company, since it’s mainly tailored for enterprise companies.

Flask: Choose Flask if you have knowledge of Python/HTML/CSS programming and you want to build your own solution completely from scratch.

#data-science #machine-learning #python #flask #dash

What is GEEK

Buddha Community

Gradio vs Streamlit vs Dash vs Flask
Carmen  Grimes

Carmen Grimes

1614070777

Gradio vs Streamlit vs Dash vs Flask

Comparing several web UI tools for data science!

Introduction

Machine learning models are exciting and powerful, but they aren’t very useful by themselves. Once a model is complete, it likely has to be deployed before it can deliver any sort of value. As well, being able to deploy a preliminary model or a prototype to get feedback from other stakeholders is extremely useful.

Recently, there has been an emergence of several tools that Data Scientists can use to quickly and easily deploy a machine learning model. In this article, we’re going to look at 4 alternatives that you can use to deploy a machine learning model: Gradio, Streamlit, Dash, and Flask.

Keep in mind that this is an opinionated article and is solely based off of my knowledge and experiences with these tools.

Summary: Which Should I Use?

Image for post

Gradio: Gradio is specifically built with machine learning models in mind. So if you want to create a web UI specifically for a machine learning model that you built, Gradio’s simple syntax and setup is the way to go.

Streamlit: Streamlit is useful if you want to get a dashboard up and running quickly, and have the flexibility to add lots of components and controls. As well, Streamlit allows you to build a web UI or a dashboard much faster than Dash or Flask.

Dash: Choose Dash if you want to be a production-ready dashboard for a larger company, since it’s mainly tailored for enterprise companies.

Flask: Choose Flask if you have knowledge of Python/HTML/CSS programming and you want to build your own solution completely from scratch.

#data-science #machine-learning #python #flask #dash

Autumn  Blick

Autumn Blick

1598839687

How native is React Native? | React Native vs Native App Development

If you are undertaking a mobile app development for your start-up or enterprise, you are likely wondering whether to use React Native. As a popular development framework, React Native helps you to develop near-native mobile apps. However, you are probably also wondering how close you can get to a native app by using React Native. How native is React Native?

In the article, we discuss the similarities between native mobile development and development using React Native. We also touch upon where they differ and how to bridge the gaps. Read on.

A brief introduction to React Native

Let’s briefly set the context first. We will briefly touch upon what React Native is and how it differs from earlier hybrid frameworks.

React Native is a popular JavaScript framework that Facebook has created. You can use this open-source framework to code natively rendering Android and iOS mobile apps. You can use it to develop web apps too.

Facebook has developed React Native based on React, its JavaScript library. The first release of React Native came in March 2015. At the time of writing this article, the latest stable release of React Native is 0.62.0, and it was released in March 2020.

Although relatively new, React Native has acquired a high degree of popularity. The “Stack Overflow Developer Survey 2019” report identifies it as the 8th most loved framework. Facebook, Walmart, and Bloomberg are some of the top companies that use React Native.

The popularity of React Native comes from its advantages. Some of its advantages are as follows:

  • Performance: It delivers optimal performance.
  • Cross-platform development: You can develop both Android and iOS apps with it. The reuse of code expedites development and reduces costs.
  • UI design: React Native enables you to design simple and responsive UI for your mobile app.
  • 3rd party plugins: This framework supports 3rd party plugins.
  • Developer community: A vibrant community of developers support React Native.

Why React Native is fundamentally different from earlier hybrid frameworks

Are you wondering whether React Native is just another of those hybrid frameworks like Ionic or Cordova? It’s not! React Native is fundamentally different from these earlier hybrid frameworks.

React Native is very close to native. Consider the following aspects as described on the React Native website:

  • Access to many native platforms features: The primitives of React Native render to native platform UI. This means that your React Native app will use many native platform APIs as native apps would do.
  • Near-native user experience: React Native provides several native components, and these are platform agnostic.
  • The ease of accessing native APIs: React Native uses a declarative UI paradigm. This enables React Native to interact easily with native platform APIs since React Native wraps existing native code.

Due to these factors, React Native offers many more advantages compared to those earlier hybrid frameworks. We now review them.

#android app #frontend #ios app #mobile app development #benefits of react native #is react native good for mobile app development #native vs #pros and cons of react native #react mobile development #react native development #react native experience #react native framework #react native ios vs android #react native pros and cons #react native vs android #react native vs native #react native vs native performance #react vs native #why react native #why use react native

Louis Jones

Louis Jones

1644207276

Python Web Applications: Dash vs Panel vs Streamlit vs Voila+IPyWidgets

Python Dashboarding Shootout and Showdown

For years, Python lagged behind other languages when it came to building interactive web applications. Python now has at least four full-featured, solid frameworks focused on dashboards and similar apps: Dash, Panel, Streamlit, and Voila+IPyWidgets. We'll hear presentations from proponents of each library, and then have a spirited debate and discussion: Which one is best for which purpose?

#python #webdev #dash #panel #streamlit #voila #ipywidget

Alec  Nikolaus

Alec Nikolaus

1602939600

Streamlit vs. Dash vs. Shiny vs. Voila vs. Flask vs. Jupyter

Data dashboards — Tooling and libraries

Nearly every company is sitting on valuable data that internal teams need to access and analyze. Non-technical teams often request tooling to make this easier. Instead of having to poke a data scientist for every request, these teams want dynamic dashboards where they can easily run queries and see custom, interactive visualizations.

Data dashboards can make data more accessible to your non-technical teams. Source: Author

A data dashboard consists of many different components. It needs to:

  • **Analyze: **Manipulate and summarize data using a backend library such as Pandas.
  • **Visualize: **Create plots and graphs of the data using a graphing library such as Bokeh.
  • **Interact: **Accept user input using a frontend library such as React.
  • **Serve: **Listen for user requests and return webpages using a web server such as Flask.

In the past, you’d have had to waste a significant amount of time writing all the “glue” code to join these components together. But with newer libraries like Streamlit and Dash, these components come in a single package.

Still, figuring out which library to use can be challenging. Here’s how they compare as well as some guidance on how to choose which one is best for your project.

[Do you want more detailed tooling comparisons that cut through the marketing-speak? Sign up to our weekly newsletter.]

Just tell me which one to use

As always, “it depends” — but if you’re looking for a quick answer, you should probably use:

  • **Dash **if you already use Python for your analytics and you want to build production-ready data dashboards for a larger company.
  • **Streamlit **if you already use Python for your analytics and you want to get a prototype of your dashboard up and running as quickly as possible.
  • Shiny if you already use R for your analytics and you want to make the results more accessible to non-technical teams.
  • Jupyter if your team is very technical and doesn’t mind installing and running developer tools to view analytics.
  • **Voila **if you already have Jupyter Notebooks and you want to make them accessible to non-technical teams.
  • **Flask **if you want to build your own solution from the ground up.

#streamlit #machine-learning #data-science #jupyter-notebook #big-data

Django vs Flask: Difference Between Django and Flask [Which is Better?]

Introduction

Python is now a widely-used programming language for server-side web development. It offers benefits such as cleaner code and flexibility, and as a developer, you have the option to use its exciting web frameworks. This way, you can bring all your app ideas to life very fast.

While developing web applications in Python, you will come across two popular web frameworks – Django and Flask. We will learn more about them in this article.

Let us understand their basics first.

#full stack development #difference between #django #django vs flask #flask