I was exploring the vertex AI AutoML feature in GCP, which lets users import datasets, train, deploy and predict ML models. My use case is to do the data pre-processing on my own (I didn't get satisfied with AutoML data preprocessing) and want to feed that data directly to a pipeline where it trains and deploys the model. Also, I want to feed the new data to the dataset. It should take care of the entire pipeline (from data preprocessing to deploying the latest model). I want insight as to how to approach this problem?
How to build custom pipeline in GCP using Vertex AI
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You can create a custom pipeline using Kubeflow Pipelines SDK v1.8.9 or higher or TensorFlow Extended v0.30.0 or higher.
If you use TensorFlow in an ML workflow that processes terabytes of structured data or text data, it is recommended that you build your pipeline using TFX.
For other use cases, we recommend that you build your pipeline using the Kubeflow Pipelines SDK. By building a pipeline with the Kubeflow Pipelines SDK, you can implement your workflow by building custom components or reusing pre-built components.
To create a Kubeflow pipeline, you can follow the next guide