Whether you are working with data or not, you can now build a machine learning model without a single knowledge of Python. How? With Google BigQueryML. Designed for data analysts or marketers working with big data this tool makes it possible to create and train ML models using millions of data rows simply with SQL. It is that simple.
Machine learning helps automate processes by enabling computers to take on work that would be previously carried out by humans. According to Accenture, Current AI technology can boost business productivity by up to 40%. Tasks such as predicting outcomes in keyword searches, detecting objects from images or creating customer segmentation are just a few examples of why machine learning has become such a trend.
But implementing a machine learning model is rather complex even if you are a data scientist or have a good knowledge of Python or R. Moving data from one to another data warehouse also makes it a time-consuming process. To simplify the development, Google released the beta version of BigQuery ML which is specifically targeted at data analysts with limited ML or programming knowledge.
BigQuery ML allows non-data scientists to build and deploy their machine learning models using SQL language. No need for programming in Python or Java, it removes all the complex and hard to understand mathematical processes of machine learning and uses a simple SQL syntax to create a model. That could look as follows:
Furthermore, the data is already stored in BigQuery, so there is no need to extract them to data warehouses making it a fast and low-cost solution. Also, the models can be run by existing BI tools and spreadsheets. This way data analysts can make use of all the favorite ML features and build and evaluate ML models directly in BigQuery.
Next to BigQuery ML, Google Cloud Platform offers other options to deploy machine learning. To choose which one to use depends on your technical skills, ML knowledge and time you want to invest.
We organise bespoke Google Cloud Platform workshops to help organisations learn as much as possible about Google BigQuery and machine learning, both in theory and practice.