blackjack-basic-strategy VS cloud-nuke

It’s an easy to use inference server for computer vision models.

The end result is a Docker container that serves a standardized API as a microservice that your application uses to get predictions from computer vision models (though there is also a native Python interface).

It’s backed by a bunch of component pieces:

* a server (so you don’t have to reimplement things like image processing & prediction visualization on every project)

* standardized APIs for computer vision tasks (so switching out the model weights and architecture can be done independently of your application code)

* model architecture implementations (which implement the tensor parsing glue between images & predictions) for supervised models that you've fine-tuned to perform custom tasks

* foundation model implementations (like CLIP & SAM) that tend to chain well with fine-tuned models

* reusable utils to make adding support for new models easier

* a model registry (so your code can be independent from your model weights & you don't have to re-build and re-deploy every time you want to iterate on your model weights)

* data management integrations (so you can collect more images of edge cases to improve your dataset & model the more it sees in the wild)

* ecosystem (there are tens of thousands of fine-tuned models shared by users that you can use off the shelf via Roboflow Universe[1])

Additionally, since it's focused specifically on computer vision, it has specific CV-focused features (like direct camera stream input) and makes some different tradeoffs than other more general ML solutions (namely, optimized for small-fast models that run at the edge & need support for running on many different devices like NVIDIA Jetsons and Raspberry Pis in addition to beefy cloud servers).

[1] https://universe.roboflow.com

* Most of the time I find Roboflow extremely handy, I used it to merge datasets, augmentate, read tutorials and that kind of thing. Basically you just create your dataset with roboflow and focus on other aspects.

For computer vision, there are 100k+ open source classification, object detection, and segmentation datasets available on Roboflow Universe: https://universe.roboflow.com

Solid website and app overall for learning more about computer vision, discovering datasets, and keeping up with advancements in the field: * https://roboflow.com/learn * https://universe.roboflow.com (datasets) | https://blog.roboflow.com/computer-vision-datasets-and-apis/ * https://blog.roboflow.com

If you're lacking training images, you can also use [Roboflow Universe](https://universe.roboflow.com) to obtain them (over 100 million labeled images available)

Roboflow | Multiple Roles | Full-time (Remote) | https://roboflow.com/careers

Roboflow is the fastest way to use computer vision in production. We help developers give their software the sense of sight. Our end-to-end platform[1] provides tooling for image collection, annotation, dataset exploration and curation, training, and deployment.

Over 100k engineers (including engineers from 2/3 Fortune 100 companies) build with Roboflow. And we now host the largest collection[2] of open source computer vision datasets and pre-trained models[3].

We have several openings available, but are primarily looking for strong technical generalists who want to help us democratize computer vision and like to wear many hats and have an outsized impact. (We especially love hiring past and future founders.)

We're hiring 3 full-stack engineers this quarter and we're also looking for an infrastructure engineer with Elasticsearch experience.

[1]: https://docs.roboflow.com

[2]: https://blog.roboflow.com/computer-vision-datasets-and-apis/

[3]: https://universe.roboflow.com

Based on what I do/use when I prepare models: A good framework for creating and improving this dataset faster is to use Roboflow Universe and search “flowers” and “bouquets of flowers” in the search bar (it’s like Google Images for CV Datasets). You can search images by subject, or metadata, and clone them directly into a free public workspace (they house up to 10k images without charge). * https://universe.roboflow.com/ * https://universe.roboflow.com/search?q=flowers * https://universe.roboflow.com/search?q=bouqets

Lots of ideas will come to mind if you look and search through open source datasets: https://universe.roboflow.com/

Open source datasets: https://universe.roboflow.com/ Model training: https://docs.roboflow.com/train Model deployment: https://docs.roboflow.com/inference/hosted-api

- https://gruntwork.io/ - https://github.com/gruntwork-io

After discussing the testing approaches suggested by the two IaC providers Terraform and Pulumi, in the next post we will take a look at the dedicated IaC testing providers takes on this topic. Here we will have a look at Gruntwork and Snyk. So, stay tuned if you are interessted!

Or your working on gruntwork.io company, this is the only the thing that makes ok all what you say here. However I believe they can make better product instead of angry chat on reddit without getting in details.

Like others have said, your infra might itself be the product.

Look at https://gruntwork.io.

They’ve made a lucrative business by selling infra scripts that others can use.

And their subscription model means they keep the scripts up to date.

Have been checking out terragrunt and terratest lately(part of https://gruntwork.io)

Gruntwork | Software Engineers (Principal, Staff) | 100% Remote/US time zones | Full-time | https://gruntwork.io/

We aim to improve humanity's most important invention: Software. Our product enables software teams to launch and maintain production-grade cloud infrastructure in days, not months. We create the building blocks that devs use to make launching in AWS with infrastructure as code 10x better.

We work with AWS, K8s, Terraform, Go, Typescript, and React/Next. We’re a small team (~20 people), but our clients include Toyota, Adobe, TicketMaster, Verizon, and lots of startups.

We are profitable, self-funded (no investors, no debt), pay salaries, equity, and bonuses according to transparent formulas, and are very focused on building a company we're proud of. We are 100% remote, with 2/3 of our team in the USA and 1/3 in Europe. We have company-wide in-person meetups every few months. We welcome applicants from all backgrounds.

Our measure of a successful Grunt is (1) think like an owner, (2) make impact, (3) communicate effectively, (4) be a good person. If this sounds like you, we're hiring!

- Principal Software Engineer

- Staff Software Engineer

Learn more at https://gruntwork.io/careers/

Most people hesitate to use cloud hosted offerings for development. First of all, most providers have a generous free tier for devs, which can get you started. Secondly I recommend using tools like cloudnuke to avoid paying for cloud resources you're not using.