├── model_C=1.0.bin └── stream_app.py Install project dependencies in a virtual environment We’ll use the Pipenv library to create a virtual Python environment and install the dependencies required to run Streamlit. The Pipenv tool automatically manages project packages thr...
American Fuzzy Lop - A good fuzzer https://lcamtuf.coredump.cx/afl/ Criterion rs - Statistics-driven Microbenchmarking in Rust https://github.com/bheisler/criterion.rs The Complete Rust Programming Reference Guide: Design, develop, and deploy effective software systems using the advanced constru...
In this code, we first import the necessary libraries. We create sample data points using NumPy arrays. Theplt.scatterfunction generates a scatter plot of the data points. Thenp.polyfitfunction is then used to calculate the slope (m) and intercept (b) of the best-fit line. Finally, we ...
In this tutorial, we take a look at running single containers and multiple containers with Compose in Azure ACI. We’ll walk you through setting up your docker context and even simplifying logging into Azure. At the end of this tutorial, you will be able to use familiar Docker commands to ...
This repository contains instructions, template source code and examples on how to serve/deploy machine learning models using various frameworks and applications such as Docker, Flask, FastAPI, BentoML, Streamlit, MLflow and even code on how to deploy your machine learning model as an android app....
Package your microservice as an API using tools like Flask or FastAPI Test your microservice thoroughly for accuracy and performance Deploy your microservice on cloud platforms like AWS, Google Cloud, or Azure Create clear documentation and usage examples for potential customers Set up a pricing mo...
Step 6: Model testing and iteration Step 7: Deploy and monitor Step 8: Continuously improve and update Step 9: Ensure data privacy and ethics Which industries can use AI apps? An in-depth look Advantages of building an AI app How does LeewayHertz’s low-code platform transform AI developmen...
Many AI tutorials often show how to deploy a small model to a web service by using theFlaskapplication framework. The problem with GPT-2 is that it’s such a huge model that most conventional advice won’t work well to get a performant app. And even if you do get it to run fast (...
When you need to scale your model to handle more predictions, or when you want to deploy multiple versions of your model for A/B testing, containers provide the agility and resource efficiency you need. To learn more about containerization and virtualization, check out this learning track: ...
A refined UX strategy for Real11 to increase their player's experience by 2X. An e-commerce platform for skincare and personalized, science-driven beauty solutions. A secure e-wallet app simplifying everyday transactions with ease. Resources ...