Developing web interfaces to interact with a machine learning (ML) model is a tedious task. With Streamlit, developing demo applications for your ML solution is easy. Streamlit is an open-source Python library that makes it easy to create and share web apps for ML and data science. As a data scientist, you may want to showcase your findings for a dataset, or deploy a trained model. Streamlit applications are useful for presenting progress on a project to your team, gaining and sharing insights to your managers, and even getting feedback from customers.

Is ChatGPT useful for Python programmers, specifically those of us who use Python for data processing, data cleaning, and building machine learning models? Let's give it a try and find out.

In my previous article, we fine-tuned a Named Entity Recognition (NER) model, trained on the wnut_17[1] dataset. In this article, we show step-by-step how to integrate this model with Streamlit and deploy it using HugginFace Spaces. The goal of this app is to tag input sentences per user request in real time. Also, keep in mind, that contrary to trivial ML models, deploying a large language model on Streamlit is tricky. We also address those challenges.

Streamlit is an open-source app framework for Machine Learning and Data Science teams. Create beautiful web apps in minutes. Streamlit is an open-source Python library that makes it easy to create and share beautiful, custom web apps for machine learning and data science that can be used to share analytics results, build complex interactive experiences, and illustrate new machine learning models. In just a few minutes you can build and deploy powerful data apps. On top of that, developing and deploying Streamlit apps is incredibly fast and flexible, often turning application development time from days into hours.

In my previous blog posts, I went through the AssemblyAI speech-to-text API. I tried its core transcription service and played with some of its cool AI-powered features: the content moderation feature that spots sensitive topics and the topic detection feature that extracts the subjects that are spoken about in each audio segment. You can check it out here. The code is also available on Github.) These experiments are all performed offline and take some time to run in order to generate the output.

To build a web app you'd typically use such Python web frameworks as Django and Flask. But the steep learning curve and the big time investment for implementing these apps present a major hurdle. Streamlit makes the app creation process as simple as writing Python scripts! In this article, you'll learn how to master Streamlit when getting started with data science. The data science process boils down to converting data to knowledge/insights while summarizing the conversion with the CRISP-DM and OSEMN data frameworks.

As a beginner, it's hard to realize how the end product of your project should look. You start with a basic machine learning pipeline, and as the project evolves, you adjust and enhance the components to meet your golden metric. To communicate your work with the world, you'd like to have a way for people to interact with the model and evaluate its performance. In this blog, we will be learning how to build a Streamlit application using only python and deploy it to a remote Heroku server. . We will use the Pneumonia-Classification project and showcase how to deploy its Streamlit app to the cloud.

You probably know about automated machine learning (AutoML). There's a good chance you have hard of the open source AutoML tool TPOT, aka your data science assistant. You may have even seen my recent article on using TPOT to optimize machine learning pipelines (you probably haven't, so here's your chance to go have a look at it... I'll wait). In any event, exploring AutoML and the knobs of machine learning optimization can make more sense when those knobs are visible and easily adjustable. In this article, we will implement a version of the TPOT example we looked at in my previous article as a Streamlit app.

In my experience, every nontrivial machine learning project is eventually stitched together with bug-ridden and unmaintainable internal tools. These tools -- often a patchwork of Jupyter Notebooks and Flask apps -- are difficult to deploy, require reasoning about client-server architecture, and don't integrate well with machine learning constructs like Tensorflow GPU sessions. I saw this first at Carnegie Mellon, then at Berkeley, Google X, and finally while building autonomous robots at Zoox. These tools were often born as little Jupyter notebooks: the sensor calibration tool, the simulation comparison app, the LIDAR alignment app, the scenario replay tool, and so on. As a tool grew in importance, project managers stepped in.