benchmarks VS MindsDB

We will be using the Metro traffic dataset 🚇 that can be downloaded from here. You are also free to use your own dataset and follow along the tutorial.

Regrading benchmarks, we have three main dataset collections we focus on currently:

1. Datasets from customers, but obviously those can’t be made public.

2. The OpenML benchmark, which is fairly limited because it’s mainly binary categories, but which is good because it’s a 3rd party, so unbiased. We have some intermediary results here (https://docs.google.com/spreadsheets/d/1oAgzzDyBqgmSNC6g9CFO...) , they are middle-of-the-road. However I think the benchmark is pretty limited, i.e. it doesn’t cover most of the kinds of inputs and almost none of the output we support

3. An internal benchmark suite which currently has 59 datasets, mainly focused around classification and regression tasks with many inputs, timeseries problems and text. Some part of it is public but opening that up is a bit difficult due to licensing issues. I’m hoping that in the next year it will grow and 90%+ of it can be made public. We benchmarkagainst older versions of mindsdb, against hand made models we try to adapt to the task, against the state of the art accuracy for the dataset (if we can find it) and a few other auto ML frameworks (well, 1, but I hope to extend that list) [see this repo for the ones we made public: https://github.com/mindsdb/benchmarks, but I'm afraid it's a bit outdated]

That being said benchmarking for us is still WIP, since as far as I can tell nobody is trying to build open source models that are as broad as what we're currently doing (for better or worst), and the closed source services offered by various IaaS providers don't really come with public benchmark results outside of marketing.

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.

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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.

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