Associate Data Practitioner
Unlock the power of your data in the cloud! Get hands-on with Google Cloud's core data services like BigQuery and Looker to validate your practical skills in data ingestion, analysis, and management, and earn your Associate Data Practitioner certification!
Practice Test
Fundamental
Practice Test
FundamentalPlan a standard ML project (e.g., data collection, model training, model evaluation, prediction)
Utilize GCP for Data Collection and Preparation
Before starting a machine learning project on GCP, you need to set up a GCP project. You can choose an existing project or create a new one depending on your team’s needs. It is important to ensure billing is enabled so that paid services run without interruption. This initial setup lays the foundation for all data workflows.
Managing data on GCP requires correct IAM roles. For example, the Storage Admin role lets you create, delete, and view Cloud Storage buckets. You can check or modify these roles in the GCP console under the IAM section. Having the right permissions ensures your data pipelines run smoothly.
GCP offers several services for data collection and preparation, each serving a specific purpose:
- Cloud Storage: Stores raw files such as CSV or JSON.
- Dataflow: Handles data cleaning and transformation at scale.
- BigQuery: Acts as a fast, managed data warehouse for querying large datasets.
Using these tools together helps you prepare data efficiently.
A common workflow involves uploading raw data to Cloud Storage, then running a Dataflow job to clean and format it. After transformation, you load the results into BigQuery for analysis or model input. This process ensures your data is cleansed, validated, and ready for the next steps in training and evaluation. Maintaining this pipeline helps you build reliable machine learning models.
Conclusion
In this section, we learned how to plan the first phase of a standard ML project on GCP by focusing on data collection and preparation. We saw how to set up a project, enable billing, and assign the right IAM roles. We also explored the roles of Cloud Storage, Dataflow, and BigQuery in storing, cleaning, and querying data. With a well-designed data pipeline, you are ready to move on to model training, evaluation, and prediction.