Machine Learning

UnionML is an open source MLOps framework that reduces the boilerplate, complexity, and friction that comes with building models and deploying them to production. Taking inspiration from web protocols, Link

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.

Content moderation is the process of learning and inferring the quality of human-generated content such as product reviews, social media posts, and ads. How do we know which are irrelevant, incorrect, or downright harmful? A related problem is detecting anomalous activity such as fraudulent transactions or malicious traffic. Link

Data powers almost all critical, customer-facing flows at Uber. Bad data quality impacts our ML models, leading to a bad user experience (incorrect fares, ETAs, products, etc.) and revenue loss. Link

Explanations to key concepts in ML Link

ModelScope is built upon the notion of “Model-as-a-Service” (MaaS). It seeks to bring together most advanced machine learning models from the AI community, and streamlines the process of leveraging AI models in real-world applications. The core ModelScope library open-sourced in this repository provides the interfaces and implementations that allow developers to perform model inference, training and evaluation. Link

ML development and deployment today suffer from fragmented and siloed infrastructure that can differ by framework, hardware, and use case. Such fragmentation restrains developer velocity and imposes barriers to model portability, efficiency, and productionization. Link

PyXAB is a Python open-source library for X-armed bandit algorithms, a prestigious set of optimizers for online black-box optimization, i.e., optimize an objective without gradients, also known as the continuous-arm bandit (CAB), Lipschitz bandit, global optimization (GO) and bandit-based black-box optimization problems. Link

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 Link

Learn the techniques we used to build a performant and efficient product categorization endpoint that will be used within our product data pipeline. Link

Definitive tutorial for advanced use of MLflow Link

Machine learning operations (MLOps) are often synonymous with large and complex applications, but many MLOps practices help practitioners build better models, regardless of the size. This talk shares best practices for operationalizing a model and practical examples using the open-source MLOps framework vetiver to version, share, deploy, and monitor models. Link

A practical framework to develop AI responsibly Link

Figuring out how to implement your ML project? Learn how other organizations did it Link