conductor
alibi-detect
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conductor | alibi-detect | |
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39 | 9 | |
12,999 | 2,074 | |
- | 1.9% | |
8.4 | 7.6 | |
4 months ago | about 1 month ago | |
Java | Python | |
Apache License 2.0 | 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.
conductor
- Netflix Conductor OSS discontinued support
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Orkes Monthly Highlights - October 2023
We celebrated a remarkable milestone in September when the Netflix Conductor GitHub repository reached 10k stars. It was a momentous achievement for our DevRel team. Just a month later, we're thrilled to announce that we've surpassed 12k stars! ⭐🎉
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4 Microservice Patterns Crucial in Microservices Architecture
Also, don’t forget to give us a ⭐ on our Netflix Conductor repo.
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The Workflow Pattern
One of my favorite workflow engines that has a really simple way to do things was not listed here, so I'll call it out - Netflix Conductor (https://github.com/Netflix/conductor).
Its capabilities comes to light when you model really complex workflows and one real value is how its all very visual not just during modeling but when running it. The history remains visible and you can even see how the whole flow evolved.
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Orkes Monthly Highlights - September 2023
Yet another significant milestone on our journey: we've proudly reached the 10,000-star mark on our Netflix Conductor GitHub repository! 🌟
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question about microservice to microservice internal only communication
Give something like https://github.com/Netflix/conductor a try to solve this -- makes it very easy to do what you are trying to achieve.
- Framework used by Netflix to orchestrate microservices
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Background Task Management on Celery and EC2
Checkout Conductor https://github.com/Netflix/conductor which is far more scalable and easy on the resources with its own Celery like queues. Fully supports writing task workers in python:
- Implementing Saga Pattern in Go Microservices
- GitHub - Netflix/conductor: Microservices orchestration engine.
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?
camunda-demo - 🗞️ Repo for this series: https://dev.to/tgotwig/getting-started-with-camunda-spring-boot-2gbi
pytorch-widedeep - A flexible package for multimodal-deep-learning to combine tabular data with text and images using Wide and Deep models in Pytorch
Activiti - Activiti is a light-weight workflow and Business Process Management (BPM) Platform targeted at business people, developers and system admins. Its core is a super-fast and rock-solid BPMN 2 process engine for Java. It's open-source and distributed under the Apache license. Activiti runs in any Java application, on a server, on a cluster or in the cloud. It integrates perfectly with Spring, it is extremely lightweight and based on simple concepts.
cleanlab - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
kestra - Infinitely scalable, event-driven, language-agnostic orchestration and scheduling platform to manage millions of workflows declaratively in code.
pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
proposals - Temporal proposals
seldon-core - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
akhq - Kafka GUI for Apache Kafka to manage topics, topics data, consumers group, schema registry, connect and more...
river - 🌊 Online machine learning in Python
Springy-Store-Microservices - Springy Store is a conceptual simple μServices-based project using the latest cutting-edge technologies, to demonstrate how the Store services are created to be a cloud-native and 12-factor app agnostic. Those μServices are developed based on Spring Boot & Cloud framework that implements cloud-native intuitive, design patterns, and best practices.
Anomaly_Detection_Tuto - Anomaly detection tutorial on univariate time series with an auto-encoder