tools

HOPSWORKS - Python-centric Feature Store

Easily build and manage feature, training and inference pipelines. Support for Python, PySpark, Spark, Flink, and SQL Link

Prefect 2 was designed for incremental adoption into your workflows. The documentation is organized to support your exploration Link

Supabase is an open source Firebase alternative. Start your project with a Postgres database, Authentication, instant APIs, Edge Functions, Realtime subscriptions, and Storage. Link

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.

5 Python Decorators I Use in Almost All My Data Science Projects

Decorators provide a new and convenient way for everything from caching to sending notifications Link

DATABUTTON - Your full (stack) potential has arrived

We’ve built a space where data skills and creativity are all you need to build insanely powerful full stack data apps. 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

OpenXLA is available now to accelerate and simplify machine learning

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

Build software collaboratively with the power of AI, on any device, without spending a second on setup Link

The best orchestration tool for MLOps: a real story about difficult choices

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.