Associate Data Practitioner

Utilize GCP for Data Collection and Preparation

Setting up a GCP project is the first step. You can use an existing project or create a new one, and you must ensure billing is enabled to use paid services. To manage data, you need the right permissions, like the Storage Admin role for creating and managing Cloud Storage buckets.

GCP provides specific services for handling data. Cloud Storage is for storing raw data files like CSVs. BigQuery is a data warehouse for running fast SQL queries on large datasets. Dataflow is for processing, cleaning, and transforming data. A typical workflow starts with raw data in Cloud Storage, uses Dataflow for cleaning, and then loads the prepared data into BigQuery for analysis and model training.

Configure and Execute Data Collection

Managing datasets in GCP prepares them for machine learning. BigQuery is central, as it allows you to create ML models using SQL. You use the CREATE MODEL statement, handle feature preprocessing, and refine the model with techniques like hyperparameter tuning.

Vertex AI is GCP's platform for managing the full ML lifecycle. It offers two main paths: AutoML for training models on tabular or image data with minimal code, and Custom Training for full control using your own frameworks and code. You can also build automated ML pipelines with Vertex AI Pipelines, which structure tasks in a workflow graph to connect different operations.

Integrating models from BigQuery ML into Vertex AI provides benefits like online model serving for real-time predictions and MLOps capabilities for continuous training and monitoring to keep models accurate.

Implement Model Training, Evaluation, and Prediction

Feature preprocessing cleans data and creates features for training. In BigQuery ML, this can be done automatically by the system or manually by defining custom logic in the CREATE MODEL statement using a TRANSFORM clause. Proper data preparation is crucial for a model to learn accurate patterns.

You train the model using the CREATE MODEL statement on your prepared dataset. During model optimization, you can use a validation set to improve performance before final testing. This step involves trying different model types, like DecisionTreeRegressor, to find the best fit for your problem.

After training, you move to model evaluation and model inference. Evaluation involves testing the model on a separate dataset and analyzing metrics like accuracy or mean squared error to ensure reliability. Once validated, you use the model for inference to generate predictions on new data, which provide actionable insights.