Google Cloud -Vertex AI and the machine learning (ML) workflow

Vertex AI is a machine learning (ML) platform that lets you train and deploy ML models and AI applications.

Let's understand each module

Data preparation:

• After extracting and cleaning your dataset, perform exploratory data analysis (EDA) to understand the data schema and characteristics that are expected by the ML model.
• Apply data transformations and feature engineering to the model, and split the data into training, validation, and test sets.
• Explore and visualize data using Vertex AI Workbench notebooks.
• For large datasets, use Dataproc Serverless Spark from a Vertex AI Workbench notebook to run Spark workloads without having to manage your own Dataproc clusters.

Model training:

Choose a training method to train a model and tune it for performance.

• To train a model without writing code, see the AutoML overview. AutoML supports tabular, image, text, and video data.
• To write your own training code and train custom models using your preferred ML framework, see the Custom training overview.
• Optimize hyperparameters for custom-trained models using custom-tuning jobs.