In ML/DL, the real modelling effort starts in production. But deploying models in production, at scale is hard - there are significant infrastructure overheads. Data scientists are forced to use low-level AWS/GCP services to build their deployment workflows. We’ve set out to make this is easy, impactful. rorodata makes it easy to go from idea to deployment quickly, users can scale as they grow.
Our goal is let data scientists focus on core business problem and abstract out the infrastructure.
While the underlying technology is key, we spend good amount of time designing for good developer experience. We work with the PyData stack on Kubernetes. So looking for engineers with good understating of Python and Unix for platform development roles.
We’re also looking for Python-based web developers for application development roles.
I'm one of the founders and would love to hear from you at [email protected].
Also beefy machine works for training jobs. But we need to deploy the models too.
Cloud provides the flexibility of choosing the hardware, many open source projects allow you to manage your dependencies and setup better. From 0 to something, cloud is better than custom.
Code, CLI gives data scientists infinite flexibility but setup, management etc. is a challenge. GUI provides very less flexibility but you will have output fast - works for simpler problems IMHO.
GUI or not data science has to move to cloud-based tools. Whether you write code in browser or CLI on local machine is matter of choice.
Consider using rorolite [0] and firefly [1] for your deep learning API projects. Deployment is becoming easier, but a lot of plumbing still needed. We see so many blogs and tutorials about writing and maintaining flask apps for this purpose. rorolite and firefly provide an elegant way to abstract all underlying engineering and make simple interface available to DL scientists. More here...