You are developing a process for training and running your custom model in production. You need to be able to show lineage for your model and predictions. What should you do?

You have trained a model by using data that was preprocessed in a batch Dataflow pipeline Your use case requires real-time inference. You want to ensure that the data preprocessing logic is applied consistently between training and serving. What should you do?

You are building an ML pipeline to process and analyze both streaming and batch datasets. You need the pipeline to handle data validation, preprocessing, model training, and model deployment in a consistent and automated way. You want to design an efficient and scalable solution that captures model training metadata and is easily reproducible. You want to be able to reuse custom components for different parts of your pipeline. What should you do?

You built a custom Vertex AI pipeline job that preprocesses images and trains an object detection model. The pipeline currently uses 1 n1-standard-8 machine with 1 NVIDIA Tesla V100 GPU.
You want to reduce the model training time without compromising model accuracy. What should you do?

You work at an ecommerce startup. You need to create a customer churn prediction model. Your company's recent sales records are stored in a BigQuery table. You want to understand how your initial model is making predictions. You also want to iterate on the model as quickly as possible while minimizing cost. How should you build your first model?