If you will ask data professionals about what is the most challenging part of their day to day work, you will likely discover their concerns around managing different aspects of data before they get to graduate to the data modeling stage.
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Daft is currently in its Alpha release phase - please expect bugs and rapid improvements to the project. We welcome user feedback/feature requests in our Discussions forums.
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Hypothesis is a Python library for creating unit tests which are simpler to write and more powerful when run, finding edge cases in your code you wouldn’t have thought to look for. It is stable, powerful and easy to add to any existing test suite.
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Jina is an MLOps framework to build multimodal AI services and pipelines then serve, scale and deploy them to a production-ready environment like Kubernetes or Jina AI Cloud. Jina handles the infrastructure complexity, making advanced solution engineering and cloud-native technologies accessible to every developer.
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Modin is a drop-in replacement for pandas. While pandas is single-threaded, Modin lets you instantly speed up your workflows by scaling pandas so it uses all of your cores. Modin works especially well on larger datasets, where pandas becomes painfully slow or runs out of memory.
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Supabase is an open source Firebase alternative. Start your project with a Postgres database, Authentication, instant APIs, Edge Functions, Realtime subscriptions, and Storage.
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WebSHAP is a JavaScript library that adapts Kernel SHAP for the Web environments. You can use it to explain any machine learning models available on the Web directly in your browser. Given a model’s prediction on a data point, WebSHAP can compute the importance score for each input feature. WebSHAP leverages modern Web technologies such as WebGL to accelerate computations. With a moderate model size and number of input features, WebSHAP can generate explanations in real time.
Machine learning has a wide range of possible applications in almost all industries. Model architecture, performance metrics improvement, and optimization of calculations have always been at the center of attention. At the same time, machine learning has not yet gone through the same stages of standardization process as software development has through the past decades. To this day, the field of machine learning does not have a single generally accepted approach to solving problems in terms of practical use of models.
This repository contains a curated list of awesome open source libraries that will help you deploy, monitor, version, scale and secure your production machine learning
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In this tutorial we’re building a fully functional Etherscan Clone, from start to finish. It will allow users get the current ether price, the latest blocks, the latest transactions, and also display all transactions for a specific wallet address.
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