Visualizing Google Cloud

CHAPTER SEVENData Science, Machine Learning, and Artificial Intelligence

The purpose of data science is to enable better decision making. Data scientists develop techniques to systematically derive insights from data. As a data scientist, you want to quickly and interactively analyze large datasets, and then share your work, communicate your insights, and operationalize your models. This process involves several key steps that are performed by different functions in companies but that can be performed by one person end-to-end. The role depends on the size and maturity of the team.

• Data Engineering — Ingest, process, and analyze real-time or batch data from a variety of sources to make data more useful and accessible. For this step, data lakes and data warehouses empower teams to securely and cost-effectively ingest, store, and analyze large volumes of diverse, full-fidelity data.
• Data Analysis — Perform data exploration, processing, and analysis in a consistent and shareable way.
• Model Development — After feature engineering, train your model and then evaluate it to see how well it is performing.
• ML Engineering — Deploy the model, serve it, and then monitor it continuously to make sure it consistently performs the way it needs to.
• Insights Activation — Use insights derived from your data to make better business decisions, influence consumer decisions, and power applications.
• Orchestration — Apply pipelines, scheduling, and CI/CD to your ML models.

In addition to offering ...