raghavsb
Joined 9 years ago
Karma
4
Submissions
9
Member since
Jan 4
Recent submissions
Comment · 1 year ago
Great, I landed on the reasoning and citations bit through trial and error and the outputs improved for sure.
Comment · 8 years ago
rorodata (https://rorodata.com) | Hyderabad, India | Software Engineers | Full-time | Onsite
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].
Comment · 8 years ago
Couldn't agree more on the data size. In most cases beefy machine work. Would on-demand (cloud) make it simple?
Also beefy machine works for training jobs. But we need to deploy the models too.
Comment · 8 years ago
Smaller datasets will work on laptop/desktop. For DL work with large datasets you need to build a GPU workstation. Moreover there setting up environment and dependencies on different hardware setups is not straight forward.
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.
Comment · 8 years ago
The talk title is quite provocative but the material discussed not so much. It is true that core data science is iterative, needs to reproducible and more recently explainable. Can you do everything in GUI? It really depends on where you see it from. Early data scientists were programmers before so they loved to code and built tooling around it. While code is still dominant we also see the rise of UI-centric tools - these allow you to build ML pipelines by snapping blocks together. I feel are chasing a "different" type of data scientist. The term data scientist itself has become quite broad.
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.
Comment · 9 years ago
Isn’t this more like Clarifai and comparables? Scikit and Keras are still lower levels of abstraction
Comment · 9 years ago
One of the collaborators on the project. Keen to get some feedback.