alibi-detect VS Appwrite

Numerous tools exist for detecting anomalies in time series data, but Alibi Detect stood out to me, particularly for its capabilities and its compatibility with both TensorFlow and PyTorch backends.

That is, to see whether a test image belongs in the distribution of the training images and to provide a routine for special cases. After a bit of reading Ive found that this is related to the field of drift detection in which I tried out alibi-detect . Whereby the training images are trained by an autoencoder and any subsequent drift will be flagged by the AE.

Wasserstein distance is not very suitable for drift detection on most problems given that the sample complexity (and estimation error) scales with O(n^(-1/d)) with n the number of instances (100k-10m in your case) and d the feature dimension (192 in your case). More interesting will be to use for instance a detector based on the maximum mean discrepancy (MMD) with estimation error of O(n^(-1/2)). Notice the absence of the feature dimension here. You can find scalable implementations in Alibi Detect (disclosure: I am a contributor): MMD docs, image example. We just added the KeOps backend for the MMD detector to scale and speed up the drift detector further, so if you install from master, you can leverage this backend and easily scale the detector to 1mn instances on e.g. 1 RTX2080Ti GPU. Check this example for more info.

Seldon | Multiple positions | London/Cambridge UK | Onsite/Remote | Full time | seldon.io

At Seldon we are building industry leading solutions for deploying, monitoring, and explaining machine learning models. We are an open-core company with several successful open source projects like:

* https://github.com/SeldonIO/seldon-core

* https://github.com/SeldonIO/mlserver

* https://github.com/SeldonIO/alibi

* https://github.com/SeldonIO/alibi-detect

* https://github.com/SeldonIO/tempo

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I work in this area and also contribute to outlier/drift detection library https://github.com/SeldonIO/alibi-detect. To tackle this type of problem, I would strongly encourage following a more principled, fundamentally (statistically) sound approach. So for instance measuring metrics such as the KL-divergence (or many other f-divergences) will not be that informative since it has a lot of undesirable properties for the problem at hand (in order to be informative requires already overlapping distributions P and Q, it is asymmetric, not a real distance metric, will not scale well with data dimensionality etc). So you should probably look at Integral Probability Metrics (IPMs) such as the Maximum Mean Discrepancy (MMD) instead which have much nicer behaviour to monitor drift. I highly recommend the Interpretable Comparison of Distributions and Models NeurIPS workshop talks for more in-depth background.

All of the above functionality and more can be easily used under a simple API in https://github.com/SeldonIO/alibi-detect.

If you haven't tried Appwrite, make sure you give it a spin. It's a open source backend that packs authentication, databases, storage, serverless functions, and all kinds of utilities in a neat API. Appwrite can be self-hosted, or you can use Appwrite Cloud starting with a generous free plan.

What is Appwrite? Appwrite is an open-source backend server that abstracts the complexity of backend development, allowing developers to focus on building their applications. It provides a wide range of services including databases, storage, functions, and authentication, all designed to work seamlessly together. This integration simplifies the development process, reducing the need for extensive configuration and integration work.

Appwrite is an open source BaaS platform that provides services like serverless functions, serverless databases, user authentication, and messaging. Since its release, it has quickly become a popular choice for building websites and applications.

Appwrite for user management, databases, and serverless functions

Appwrite: Open-source backend server for web and mobile developers.

Today, this ecosystem is going strong with new providers like Hasura, AppWrite and Supabase powering millions of projects. There are a few reasons people choose this style of hosting, especially if they are more comfortable with frontend development. BaaS lets them set up a database in a secure way, expose some business logic on top of the data, and connect via a dev-friendly SDK from their app or website code to save data easily. These modern tools build a blend of managed database with curated plugins such as authentication, great admin dashboards, and function as a service type capability - all in one package, and often offered as a integrated hosted service.

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Joins - see Future of Queries - MariaDB supports json joins, so definitely possible!

Wow, looks nice! I almost felt like I could understand Bitcoins code xD

Could you do Appwrite? https://github.com/appwrite/appwrite

I'm not affiliated to them, just wanted to get started hacking it.