Setup & Configuration
How to get a trial version of dataiku to complete project for advanced designer certification course
Last answer by Rahul_Raj123How to register online. Will it be a free version?
Last answer by Rahul_Raj123How to register online. Will it be a free version?
- Hi, I was recently trying to install the dataiku api locally for some testing purposes but was met with some errors. Installation seemed to be working fine but the moment I tried to run this code:`fro…
Last answer by TurribeachDataiku can easily be installed on-prem or even on Cloud (your own cloud that is). So you can easily have local access to it. Another option it will work is to write a Python recipe to interface with the LLM mesh and execute that recipe remotely using the Dataiku Python API and access the outputs as a result dataset. I also think you should be able to create a wrapper API over the LLM mesh as a custom Project Dataiku API which you can deploy to the Dataiku API node and again access remotely. But you will always need to go via Dataiku as that’s what the LLM mesh is giving you, an easy way to interface with LLM models.
Also keep in mind that the LLM mesh is simply a wrapper over other LLM models which are typically hosted via third party APIs. So you typically depend on those third party models and APIs, usually hosted on internet facing services.Last answer by TurribeachDataiku can easily be installed on-prem or even on Cloud (your own cloud that is). So you can easily have local access to it. Another option it will work is to write a Python recipe to interface with the LLM mesh and execute that recipe remotely using the Dataiku Python API and access the outputs as a result dataset. I also think you should be able to create a wrapper API over the LLM mesh as a custom Project Dataiku API which you can deploy to the Dataiku API node and again access remotely. But you will always need to go via Dataiku as that’s what the LLM mesh is giving you, an easy way to interface with LLM models.
Also keep in mind that the LLM mesh is simply a wrapper over other LLM models which are typically hosted via third party APIs. So you typically depend on those third party models and APIs, usually hosted on internet facing services.