BigQuery ML
1. BigQuery ML
Machine learning on large data sets requires extensive programming and knowledge of ML frameworks. These requirements restrict solution development to a very small set of people within each company, and they exclude data analysts who understand the data but have limited machine learning knowledge and programming expertise. Although BigQuery started solely as a data warehouse, over time it has evolved to provide additional features that support the data to AI life cycle. BigQuery ML democratizes the use of machine learning by empowering data analysts, but primary data warehouse users, to build and run models by using existing business intelligence tools and spreadsheets. Predictive analytics can guide business decision making across the organization. Using Python or Java to program an ML solution isn't necessary. Models are trained and access directly in BigQuery by using SQL, which is a language familiar to data analysts. BigQuery ML brings machine learning to the data. It reduces complexity because fewer tools are required. It also increases speed of production because moving and formatting large amounts of data for Python-based ML frameworks is not required for model training in BigQuery. BigQuery ML also integrates with Vertex AI, Google Cloud's end to end AI and ML platform. When BigQuery ML models are registered to the Vertex AI model registry, they can be deployed to endpoints for online prediction.2. Let's practice!
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