Google Vertex AI - Data Capture Overview and Pre-Processing

Описание: In this video, we would continue our use case and implementation on Vertex AI from our last week video.

In our earlier video, we talked about Vertex AI and went through how AI can transform fashion retail.

In this week episode, I would demonstrate how we can realize the image categorisation use case from the earlier episode using Vertex AI.
This video is broken down into 3 series - Getting started and Data Capture,
Training the model and looking at the results and last being deploying and testing the model.

To get STARTED, the first step is prepare your training data for your AI solution.

Usually, you would have a data pipeline for capturing your data and preprocessing it into a format that can be trained. For instance, for our AI solution for dress categorisation, we would source around 200 images
for each category. Now, the image resolution and sizes might be different for these image, so a part of preprocessing you would get all images into a standard size and resolution.

For instance, take another example where you want to extract sentiments using NLP from various unstructured public document. So you use some crawler to crawl the documents from various public sources, the unstructured document types would be different , PDF, word document and so on, and within PDF’s also you would have various format like tables etc.. So you would need to build a pre-processing pipeline to parse and extract the relevant information and get into the format (probably sentences) that can be fed into model for training.

Watch the Vertex AI video to deep dive into the data capture process.

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