bodywork-pipeline-with-aporia-monitoring
ML-Workspace
| bodywork-pipeline-with-aporia-monitoring | ML-Workspace | |
|---|---|---|
| 1 | 7 | |
| 4 | 3,324 | |
| - | 0.4% | |
| 0.0 | 2.7 | |
| almost 2 years ago | 6 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | Apache License 2.0 |
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bodywork-pipeline-with-aporia-monitoring
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Calling Aporia from Bodywork Pipelines to Monitor Models in Production
Monitoring models for drift and degradation is not easy - theoretically or practically. In this example project we show to outsource these problems to Aporia’s model monitoring platform, by using their Python client from within a Bodywork pipeline.
ML-Workspace
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[D] I recently quit my job to start a ML company. Would really appreciate feedback on what we're working on.
Also check out: https://github.com/ml-tooling/ml-workspace, it a nice open source project with lots of packages ready to use.
- ML-Workspace
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Coding for machine learning on Tab S8?
The other option - no reason why you couldn't host something on the desktop machine - web based IDE like R-Studio or Python - have a look at ml-workspace - https://github.com/ml-tooling/ml-workspace that runs in Docker and would provide interfaces for both Python and R, VSCode as well as a GPU accelerated variant for doing Tensorflow etc - either Windows or Linux can support Docker containers (Linux is less trouble apparently - I only have played with it in Linux personally)
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Dynamically spin up VM (based on specific HTTPS request) and stop it once session is over?
It will be a web based IDE dev kit (like Jupyter Hub, or JupyterLab) if you are familiar with them)
- All-in-One Docker Based IDE for Data Science and ML
- Visual Studio Code now available as Web based editor for GitHub repos
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[P] Install or update CUDA, NVIDIA Drivers, Pytorch, Tensorflow, and CuDNN with a single command: Lambda Stack
I'll stick with https://github.com/ml-tooling/ml-workspace, is a docker with all tools installed, also the option of using GPU, so I think is better than only for debian. This way anyone can use it.
What are some alternatives?
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
JupyterLab - JupyterLab computational environment.
VevestaX - 2 Lines of code to track ML experiments + EDA + check into Github
Gitpod - DEPRECATED since Gitpod 0.5.0; use https://github.com/gitpod-io/gitpod/tree/master/chart and https://github.com/gitpod-io/gitpod/tree/master/install/helm
bodywork-pymc3-project - Serving Uncertainty with Bayesian inference, using PyMC3 with Bodywork
keytotext - Keywords to Sentences
ml-pipeline-engineering - Best practices for engineering ML pipelines.
self-hosted - Sentry, feature-complete and packaged up for low-volume deployments and proofs-of-concept
bodywork - ML pipeline orchestration and model deployments on Kubernetes.
Code-Server - VS Code in the browser
MLOps - End to End toy example of MLOps
cocalc-docker - DEPRECATED (was -- Docker setup for running CoCalc as downloadable software on your own computer)