l5kit VS alibi-detect

Level 5 | Self-driving Research | Palo Alto, US & London, UK & Tokyo, Japan | Full-Time + Interns | Onsite (Hybrid - 2 days in p/w - currently closed)

Level 5 at Woven Planet develops real-time car automation solutions via applied Machine Learning and Computer Vision (SFM, SLAM, 3D Perception etc). The system is adding self-driving capabilities to vehicles with the end goal of providing autonomy features to all cars from the largest car company in the world, Toyota. For more information please read through https://www.self-driving-cars.org

The platform at Level 5 is written in Python & C++. We are an AWS environment with additional use of NumPy, PyTorch, GRPC, Kafka, Kubernetes, Terraform, SageMaker, Spark, Postgres & others. We work in a OneBox cloud environment with continuous deployment and are firm believers in the benefits of open source (https://github.com/woven-planet/l5kit). We apply multiple flavours of ML to petabytes of data; such as Deep Learning, Transformers, Neural Networks & Reinforcement Learning. If you are interested in applying Machine Learning (ML) to real world data - look no further. https://www.self-driving-cars.org/datasets

If you like the idea of working on some of the most challenging problems in applied computer science. We are looking for talent across Data, Computer Vision, Machine Learning, Infrastructure, Research - and of course Software Engineering. Please find our jobs at https://boards.greenhouse.io/l5

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

We are hiring for a range of positions, including software engineers(go, k8s), ml engineers (python, go), frontend engineers (js), UX designer, and product managers. All open positions can be found at https://www.seldon.io/careers/

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.