tools

Discover over 400 tools to build awesome things. Link

Accelerate work and unlock potential with powerful apps that connect your data, workflows and teams. Link

Materialize is a streaming database with a SQL API. However, despite the fact that Materialize uses SQL idioms and can process data from databases, it actually has very little in common with “databases” as most people think of them. Link

Airflow is one of my favorite tools that I frequently use to setup and manage data science pipelines. The Airflow UI gives us a clear picture of the DAGS and its current status. I may be wrong here but from my experience, I have seen that Airflow on a single machine is not scalable. Thus, to scale Airflow, we can use Kubernetes. Link

TorchMetrics is a really nice and convenient library that lets us compute the performance of models in an iterative fashion. It’s designed with PyTorch (and PyTorch Lightning) in mind, but it is a general-purpose library compatible with other libraries and workflows. Link

This project provides source code of example applications for people to get started using AWS Serverless Application Repository. Link

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Covalent provides a unified API bringing visibility to billions of blockchain data points. Link

This repository demonstrates how a simple voice transfer app can be created using Streamlit. The code for this demo is based on the repository for Real-Time-Voice-Cloning. Link

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Integromat lets you connect apps and automate workflows in a few clicks. Move data between apps without effort so you can focus on growing your business. Link

Jupyter Notebook is probably the most popular tool used by Data Scientists. It allows mixing code, text, and inspecting the output in one document. This is something that is not possible with some other programming IDEs. However, the vanilla version of the Jupyter notebooks is not perfect. In this article, we will show you how to make it slightly better by installing some useful extensions. Link