refinery VS awesome-production-machine-learning

You definitely forgot https://www.kern.ai/ :)

We will build the web scraper in Kern AI workflow, labeled our news articles in refinery, and then enrich the data with gates AI. After that, we will use workflow again to send out the predictions and the enriched data via a webhook to Slack. If you'd like to follow along or explore these tools on your own, you can join our waitlist here: https://www.kern.ai/

A platform has been developed by the German startup Kern AI for NLP developers and data scientists to not only control the labeling process but also automate and orchestrate tangential tasks and enable them to address low-quality data that comes their way. Several companies exist substantively to power this labeling process.

Fast forward to July ‘22 (after many further product iterations and a full redesign), we open-sourced our product under a new name: Kern AI refinery (the origin of the name is very simple: we want to improve, i.e., refine, the foundation for building models).

Get it for free here: https://github.com/code-kern-ai/refinery

Containers are amazing for building applications. Because they allow you to pack up a programm together with all it's dependencies and execute it wherever you like. That is why our application consists of 20+ individual containers, forming our data-centric IDE for NLP, which you can check out here: https://github.com/code-kern-ai/refinery.

Today we launched bricks, an open-source library which provides enrichments for your natural language processing projects. Our main goal with bricks is to shorten the amount of time that you need from idea to implementation. Bricks also seamlessly integrates into our main tool, the Kern AI refinery.

This blog post will share our experience with fine-tuning sentence embeddings on a commonly available dataset using similarity learning. We additionally explore how this could benefit the labeling workflow in the Kern AI refinery. To understand this post, you should know what embeddings are and how they are generated. A rough idea of what fine-tuning is also helps. All the code and data referenced in this post is available on GitHub.

We've built our open-source IDE for data-centric NLP with the belief that data scientists and engineers know best what kind of framework they want to use for their model building. Today, we'll show you three new adapters for the SDK.

One trove of treasures is the awesome-production-machine-learning repository on GitHub. This curated list provides a multitude of frameworks, libraries, and software designed to facilitate various stages of the ML lifecycle.

There is a cool, gigantic list for MLOps that I can recommend: https://github.com/EthicalML/awesome-production-machine-learning

There are a lot of frameworks and specific tools out there that try to make production ML projects viable; from specific like Airflow (orchestrating jobs) and MLflow (experiment tracking) to more complex ones like Kubeflow. You can have a grasp here.

https://github.com/EthicalML/awesome-production-machine-lear...

https://github.com/EthicalML/awesome-production-machine-learning I think you will find most of the tools to visualize the model on this link.

2/ https://github.com/EthicalML/awesome-production-machine-learning