engineering
alibi-detect
engineering | alibi-detect | |
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3 | 9 | |
36 | 2,085 | |
- | 1.6% | |
0.0 | 7.6 | |
over 1 year ago | 11 days ago | |
Python | ||
- | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
engineering
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Ask HN: Who is hiring? (January 2022)
Slim.AI | Fullstack and Backend Engineers | REMOTE, international or Seattle/Bellevue/WA | Full-time | Golang, Node.js, Vue.js/Nuxt.js
I'm the founder and CTO at Slim.AI. We are a well funded seed stage startup (9M+) in the developer tooling space. Our mission is to simplify and accelerate the containerized app delivery (it's too hard, too complicated and with too much manual work). We are about to transition to the next phase and we are expanding our engineering team.
Our engineering team is the innovation engine for our product because we are building a solution to solve our own problems creating and running containerized cloud-native applications.
We use Golang, Node.js Serverless/Lambda and containers. We have frontend, backend and fullstack roles ( https://github.com/slim-ai/engineering ).
Our engineering principles:
* We use what we build.
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Ask HN: Who is hiring? (December 2021)
Slim.AI | Backend and Fullstack Engineers | REMOTE, international or Seattle/Bellevue/WA | Full-time | https://github.com/slim-ai/engineering
We are a well funded seed stage startup (9M+) in the developer tooling space on a mission to redefine how DevOps is done for containerized apps (it's too hard, too complicated and with too much manual work). We are about to transition to the next phase and we are expanding our engineering team.
Our engineering team is the innovation engine for our product because we are building a solution to solve our own problems creating and running containerized cloud-native applications.
We use Golang, Node.js Serverless/Lambda and containers. Take a look at the backend ( https://github.com/slim-ai/engineering/blob/master/roles/bac... ) and fullstack ( https://github.com/slim-ai/engineering/blob/master/roles/ful... ) roles and our engineering principles to see if the role and how we do engineering looks interesting to you ( https://github.com/slim-ai/engineering#engineering-principle... ).
Email me at [email protected] if you'd like to learn more.
P.S.
And take a look at DockerSlim ( https://github.com/docker-slim/docker-slim ) if you are interested in working on the open source project that powers our SaaS.
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Ask HN: Who is hiring? (January 2021)
Slim.AI | REMOTE or Seattle | Full-time | Developer Experience Lead | https://github.com/slim-ai/engineering
Do you enjoy working with lots of different applications stacks? Do you like helping others? Do you want to build lots of different applications? Are you interested in contributing to open source?
We are a funded seed stage startup in the developer tooling and DevOps space empowering developers to build and run their cloud-native applications. The current product is focusing on containers and the friction around them.
We are building a brand new engineering team. We are developer friendly, low on process with no mind-numbing bureaucracy or micromanagement. We are looking for people who'll be excited to be a part of the engineering team in an early stage startup during its inception phase building modern cloud-native applications the right way.
You can find out more about the mission, how we work and the roles here: https://github.com/slim-ai/engineering
Email me at [email protected] if you'd like to learn more.
alibi-detect
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
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.
- Looking for recommendations to monitor / detect data drifts over time
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[D] Distributions to represent an Image Dataset
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.
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[D] Which statistical test would you use to detect drift in a dataset of images?
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.
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Ask HN: Who is hiring? (January 2022)
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/
- What Machine Learning model monitoring tools can you recommend?
- Ask HN: Who is hiring? (December 2021)
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[D] How do you deal with covariate shift and concept drift in production?
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.
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[D] Is this a reasonable assumption in machine learning?
All of the above functionality and more can be easily used under a simple API in https://github.com/SeldonIO/alibi-detect.
What are some alternatives?
pulsechain-testnet
pytorch-widedeep - A flexible package for multimodal-deep-learning to combine tabular data with text and images using Wide and Deep models in Pytorch
orchest - Build data pipelines, the easy way 🛠️
cleanlab - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
Lean and Mean Docker containers - Slim(toolkit): Don't change anything in your container image and minify it by up to 30x (and for compiled languages even more) making it secure too! (free and open source)
seldon-core - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
MLServer - An inference server for your machine learning models, including support for multiple frameworks, multi-model serving and more
river - 🌊 Online machine learning in Python
zenml - ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.
Anomaly_Detection_Tuto - Anomaly detection tutorial on univariate time series with an auto-encoder