Top 4 model-management Open-Source Projects
Open source platform for the machine learning lifecycleProject mention: [RFP] Product idea for BYOD data science platform | reddit.com/r/datascience | 2022-06-17
I think this might be useful to let small-mid-sized DS teams to better utilize their computing resources (e.g., if you have multiple GPU workstations and rely on assigning each one to people to SSH onto, this might be for you) by pooling them and providing a service like Jupyterhub on-top to provide a unified entry point to conduct their work using notebooks. Addons like MLFlow and Kubeflow can be added with single-click as well once the platform is up.
:rocket: Build and manage real-life data science projects with ease!Project mention: AWS Summit 2022 Australia and New Zealand - Day 2, AI/ML Edition | dev.to | 2022-05-20
As a result of their new DS framework (based on a Metaflow - a DS framework built at Netflix and AWS SageMaker Pipelines), they were able to free up their DS resources so that Software Developers were now trained and equipped to tackle their normal DS projects, at a ratio of 70% DS/ML work was now completed by developers. This leaves the 30% meatier and more difficult problems for the Data Scientists to tackle.
Less time debugging, more time building. Scout APM allows you to find and fix performance issues with no hassle. Now with error monitoring and external services monitoring, Scout is a developer's best friend when it comes to application development.
The Unified Model Serving Framework 🍱Project mention: How to Build a Machine Learning Demo in 2022 | dev.to | 2022-01-16
Using a general-purpose framework such as FastAPI involves writing a lot of boilerplate code just to get your API endpoint up and running. If deploying a model for a demo is the only thing you are interested in and you do not mind losing some flexibility, you might want to use a specialized serving framework instead. One example is BentoML, which will allow you to get an optimized serving endpoint for your model up and running much faster and with less overhead than a generic web framework. Framework-specific serving solutions such as Tensorflow Serving and TorchServe typically offer optimized performance but can only be used to serve models trained using Tensorflow or PyTorch, respectively.
Docker for Your ML/DL Models Based on OCI ArtifactsProject mention: [P] ormb: Docker for Your Models, Help You Manage Models Better | reddit.com/r/MachineLearning | 2022-04-18
model-management related posts
AWS Summit 2022 Australia and New Zealand - Day 2, AI/ML Edition
1 project | dev.to | 20 May 2022
[P] ormb: Docker for Your Models, Help You Manage Models Better
1 project | reddit.com/r/MachineLearning | 18 Apr 2022
Simplest way to run large batch jobs in the cloud?
1 project | reddit.com/r/dataengineering | 19 Feb 2022
2 projects | news.ycombinator.com | 23 Jan 2022
Help on understanding mlops tools.
1 project | reddit.com/r/mlops | 6 Oct 2021
A few reasons why internal product management is awesome and not a downgrade
1 project | reddit.com/r/ProductManagement | 23 Aug 2021
Why do so many people think Python is easier to productionize than R?
2 projects | reddit.com/r/datascience | 4 Apr 2021
What are some of the best open-source model-management projects? This list will help you:
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