The document outlines the key steps in the artificial intelligence workflow, emphasizing data preparation as the most crucial stage to train a model with clean and labeled data. Following this, modeling occurs where the prepared data serves as input for the model to learn. Finally, deployment involves preparing the model for the target hardware, requiring collaboration with design engineers to ensure compatibility.

1. STEP
3:
arificial intelligence process
the
Data Preparation
Most important step in the AI
workflow. To train a model, you
should begin with clean, labeled
data, as much as you can gather.
Deployment
Once you are ready to deploy, the
target hardware is next i.e. readying the
model in the final language in which it
will be implemented. This step typically
requires design engineers to share an
implementation-ready model, allowing
them to fit that model into the
designated hardware environment.
STEP
1:
STEP
3:
STEP
2:
AI Modeling
Once the data is clean and
properly labeled, it’s time to move
on to the modeling stage of the
workflow, which is where data is
used as input, and the model
learns from that data.
Simulation and Test
Once the data is clean and
properly labeled, it’s time to move
on to the modeling stage of the
workflow, which is where data is
used as input, and the model
learns from that data.
STEP
4: