giskard VS MindsDB

Unchecked biases in LLMs can inadvertently perpetuate harmful stereotypes or produce misleading information, which in turn can produce severe consequences. In this article, we'll demonstrate how to evaluate your LLMs using an open source model testing framework, Giskard. 🤓

Hi! I’ve been working on this automatic scanner for ML models to detect issues like underperforming data slices, overconfidence in predictions, robustness problems, and others. It supports all main Python ML frameworks (sklearn, torch, xgboost, …) and integrates with the quality assurance solution we are building at Giskard AI (https://giskard.ai) to systematically test models before putting them in production.

It is still a beta and I would love to hear your feedback if you have the time to try it out.

We have quite a few tutorials in the docs with ready-made colab notebooks to make it easy to get started.

If you are interested in the code:

https://github.com/Giskard-AI/giskard/tree/main/python-clien...

Sure! Benjamini-Hochberg is a very good recommendation, much simpler than the alpha investing procedures I mentioned which makes it easily to implement in our case. I will give it a try, if there’s an easy way to set this up it could be included in some of the next releases. I’ll let you know. FYI, I added this to our issue tracker.

This is super interesting! Thanks for sharing. We're also working on this research field from an open-source angle (https://github.com/Giskard-AI/giskard)

Code repository: https://github.com/Giskard-AI/giskard

Check us out on GitHub.

If your data lacks uniform time intervals between consecutive entries, QuestDB offers a solution by allowing you to sample your data. After that, MindsDB facilitates creating, training, and deploying your time-series models.

MindsDB is an open-source AI platform for developers that connects AI/ML models with real-time data. It provides tools and automation to easily build and maintain personalized AI solutions.

MindsDB combines both AI and SQL functions in one; users can create, train, optimize, and deploy ML models without the need for external tools. Data analysts can create and visualize forecasts without having to navigate the complexities of ML pipelines.MindsDB is open-source and works with well-known databases like MySQL, Postgres, Redit, Snowflakes, etc.

Mindsdb is a good example. It abstracts everything related to an AI workflow as "virtual tables". For example, you can import OpenAI API as a "virtual table":

Repo : https://github.com/mindsdb/mindsdb

MindsDB is an AI Automation platform for building AI/ML powered features and applications. It works by connecting any data source with any AI/ML model or framework and automating how real-time data flows between them. MindsDB is integrated with LlamaIndex, which makes use of its data framework for connecting custom data sources to large language models. LlamaIndex data ingestion allows you to connect to data sources like PDF’s, webpages, etc., provides data indexing and a query interface that takes input prompts from your data and provides knowledge-augmented responses, thus making it easy to Q&A over documents and webpages.

Now, imagine if you can deploy these highly trained models in your database to get insights, make predictions, understand your users, auto-generate content, and more. MindsDB makes this possible! MindsDB is an open-source AI database middleware that allows you to supercharge your databases by integrating various machine learning (ML) engines.