Most machine learning follows an iterative lifecycle of preparing data, designing features, training the model, evaluating performance, deploying to an inference pipeline, and monitoring over time.
Data preparation
The lifecycle typically begins with data preparation. The team collects raw data, removes inconsistent values, handles missingness, and joins related tables to create a training data set that reflects the problem the model is trying to solve. For example, in a customer churn model, the training data set might include account attributes, product usage, support interactions, billing history, and labels that indicate whether the customer has renewed.
Feature engineering
Feature engineering transforms prepared data into signals that models can learn from, converting raw timestamps, transaction sequences, and unstructured text into derived values such as recency windows, rolling averages, and embeddings. This step often requires domain context. This is because the useful signal is rarely the raw column itself, but how its values behave over time, across accounts, or in relation to the outcome the model is trying to predict. For example, in a churn model, the total number of logins is less important than the sudden drop in usage after onboarding. In a fraud model, the transaction amount means nothing without the context of whether that amount is unusual for the account, merchant, or location.
Training the model
During model training, the algorithm adjusts internal parameters to reduce the error relative to the training goal. For many models, the objective is captured by a loss function, and model training optimization techniques such as gradient descent adjust the model weights to reduce loss. Data scientists also tune hyperparameters such as learning rate, tree depth, and regularization strength. These parameters shape how the model is trained, but they are not learned directly from the data.
evaluation
Evaluation tests whether the model generalizes beyond the training data. Teams typically use a validation set or cross-validation process to compare candidate models, check for overfitting, and measure performance with metrics such as precision, recall, F1 score, root mean square error, and area under the curve, depending on the use case.
introduction
Deployment moves the model into the inference pipeline. With batch inference, the model scores records according to a schedule, such as overnight churn prediction or weekly demand prediction. With real-time inference, the model returns predictions during active workflows, such as scoring transactions while the customer is still checking out.
monitoring
Watching ends the loop. A model that performs well during evaluation can drift in production if inputs change, labels change, or the upstream pipeline breaks. Snowflake’s ML Observability documentation states that model behavior can change over time due to input drift, stale training assumptions, data pipeline issues, and changing traffic patterns.
