Thanks YC! For multiple languages, Zerve supports full language interoperability with Python, R and SQL i.e. you could connect an R code block to a Python code block and GG plot and pandas dataframe for example. Deployment in Zerve can then happen in two ways:
1) You can build and deploy directly from within Zerve through a lambdas based API. Benefit of this is that its super scalable and you aren't limited to any number of fixed parallel process .
2) If you have your own production pipeline setup already, we wanted to make that handover as seamless as possible so you can export the code you want to deploy as a docker file.
Hope this helps and let me know if you have any more questions :)
It is an excellent product. I have personally used it and find it to be robust. My Python scripts executed flawlessly. I tested it in the beta stage and I am eagerly looking forward to the launch.
One of the best ML tools available in the market. It’s very easy to use, have great features like API deployment by single click, ML model pipeline etc.
I started using Zerve and I am enjoying the code-first widget interface. It allows me to parallelize the data science project pipelines and also integrate SQL and Python into the same canvas. I am planning on suggesting Zerve AI to rest of my team at the workplace.
