Sequencing a machine learning project from raw data to served predictions

A standard ML project on Google Cloud follows a repeatable lifecycle: define the business problem, collect and prepare data, train and evaluate a model, then deploy it for predictions and monitor performance. Google Cloud provides both AutoML (low-code, for users without ML expertise) and custom training (via Vertex AI, for data scientists) to support this workflow. Understanding the sequence and purpose of each phase is key for the exam, even without deep ML implementation knowledge.

1 · Learn the must-know

• 1The typical ML workflow order is: define objective/problem, extract/collect and prepare data, train the model, evaluate the model, deploy the model, and serve predictions (batch or online).
• 2Data collection and preparation (cleaning, labeling, splitting into training/validation/test sets) typically consumes the majority of project time and directly determines model quality.
• 3Vertex AI is Google Cloud's unified platform for the entire ML lifecycle, offering AutoML for automated model building and custom training for code-based model development.
• 4Model evaluation uses metrics appropriate to the problem type (e.g., accuracy, precision, recall, F1 for classification; RMSE/MAE for regression) and should be assessed against a held-out test set never used in training.
• 5Predictions can be served as batch predictions (large datasets, no immediate response needed) or online predictions (low-latency, real-time inference via deployed endpoints).
• 6A production ML project is iterative and includes ongoing monitoring for data/model drift, requiring periodic retraining, not a one-time train-and-deploy event.

2 · Check your understanding