Feb 9, 2023 · Tabular data, Sklearn, building Docker Image and deploying it to Heroku and Kubernetes. To build not a REST API (FastAPI) app, but a Streamlit app, just use mlem serve streamlit, mlem build --server streamlit, and mlem deploy --server streamlit: Get Started

Oct 17, 2024 · In this article, we are going to deep dive into model deployment. We will first build a loan prediction model and then deploy it using Streamlit. Let’s start with understanding the overall machine learning lifecycle, and the different steps that are involved in creating a machine learning project.

Oct 3, 2019 · I have built a tensorflow object detection model that basically inputs videos and outputs the objects detected. My biggest challenge has been in extracting and interpreting that data generated by the model.

Jul 16, 2021 · Ever since Streamlit launched their application framework towards the end of 2019, Machine Learning Engineers and Data Scientists have been flocking to it to create visually appealing...

Nov 9, 2023 · My proposed architecture would be a Streamlit frontend, connected to a Django backend with 3 applications: one for general backend tasks and communication with the frontend and DB (maybe via REST), one for Tensorflow related tasks, and one for authentication.

Apr 24, 2024 · Crafting the model Architecture for Neural Network are stacked sequentially, The network consist of input, hidden, and output layers. Input layer: Receives the preprocessed loan applicant...

Serve TensorFlow ML models with TF-Serving and then create a Streamlit UI to use them! This is a pretty simple Streamlit UI to expose the functionality of a TensorFlow image classification CNN served using TensorFlow Serving.

Nov 9, 2023 · My proposed architecture would be a Streamlit frontend, connected to a Django backend with 3 applications: one for general backend tasks and communication with the frontend and DB (maybe via REST), one for Tensorflow related tasks, and one for authentication.

Jul 23, 2023 · Here's a brief overview of how the project works: Train a TensorFlow model: The project includes a Jupyter notebook that demonstrates how to train a simple TensorFlow model on the MNIST dataset. Export the model for serving: The trained model is exported in a format that can be served using TensorFlow Serving.

Sep 17, 2023 · In this tutorial, we’ll explore the power of Streamlit, a user-friendly Python framework, to create interactive data-driven apps. Our goal is to guide you through the process of building an...