zpy VS metaflow

This is actually a great problem for synthetic data. You can make a synthetic dataset of colored cubes on different backgrounds using something like Unity or zpy (shameless plug). The synthetic data will be much larger and more varied than one you create & label by hand.

I'm sort of in the same boat. I just discovered this very interesting package called zpy that's meant to help automate turning synthetic conditions into training data. I have some experience with Python and am fairly new to Blender, but I could probably get as far as making a good segmented dataset.

Thanks for getting this far! If you’re interested in 3D and what it can do for synthetic data, check out our open-source data development toolkit zpy. Everything you need to generate and iterate synthetic data for computer vision is available for free. Your feedback, commits, and feature requests are invaluable as we continue to build a more robust set of tools for generating synthetic data. In the meantime, if you need our support with a particularly tricky problem, please reach out.

I work at Zumo Labs, where we create synthetic data for computer vision using zpy (github.com/ZumoLabs/zpy).

The easiest way to get started with zpy (available on GitHub) is to follow the steps outlined in this short video tutorial series.

Tools that make synthetic data generation easy are fundamentally changing the way machine learning work is done. Iterating and improving the dataset over the course of a project is more important to project success than iterating the model architecture. That's why we are releasing zpy, an open source synthetic data toolkit. All developers should have the option of working with dynamic data rather than static data.

https://github.com/ZumoLabs/zpy We just released our open source synthetic data toolkit built on top of Blender. Our package makes it easy to design and generate synthetic data for computer vision projects. Let us know what you think and what features you want us to focus on next!

https://github.com/ZumoLabs/zpy We just released our open source synthetic data toolkit built on top of Blender. Our package makes it easy to design and generate synthetic data for computer vision projects. Let us know what you think and what features you want us to focus on next!

I would recommend the following: - https://www.mage.ai/ - https://dagster.io/ - https://www.prefect.io/ - https://metaflow.org/ - https://zenml.io/home

1) I've been looking into [Metaflow](https://metaflow.org/), which connects nicely to AWS, does a lot of heavy lifting for you, including scheduling.

even for people who don't have an ML background there's now a lot of very fully-featured model deployment environments that allow self-hosting (kubeflow has a good self-hosting option, as do mlflow and metaflow), handle most of the complicated stuff involved in just deploying an individual model, and work pretty well off the shelf.

They had to figure out video compression that worked at the volume that they wanted to deliver. They had to build and maintain their own CDN to be able to have a always available and consistent viewing experience. Don’t even get me started on the resiliency tools like hystrix that they were kind enough to open source. I mean, they have their own fucking data science framework and they’re looking into using neural networks to downscale video.. Sound familiar? That’s cause that’s practically the same thing as Nvidia’s DLSS (which upscales instead of downscales).

Check out Metaflow by Netflix

Github Actions, Metaflow and AWS SageMaker are awesome technologies by themselves however they are seldom used together in the same sentence, even less so in the same Machine Learning project.

It's undeniable that leadership is instrumental in any company and project success, however I was intrigued with one of their ML tool choices that helped them reach their goal. I was so curious about this choice that I just had to learn more about it, so in this article will be talking about a sound strategy of effectively scaling your AI/ML undertaking and a tool that makes this possible - Metaflow.