Big data is a most used data analysis technique in the modern world. Big data analysis in itself is a big procedure that helps the organizations in getting crisp and analyzed data with minimum efforts. Several steps are involved in data processing through big data technique; even many tools are utilized by the organizations, so that they can get simple and crisp data as and when required. A similar tool is launched by the Google to build a predictive model with SQL. This article will help you in understanding this tool in depth.

What is BigQuery ML Tool?

You might be familiar with the fact that if you want to do efficient data analysis then computation must be done at the place, where data lives. Technically it does mean that data analysis must be done in a database where R, Java, Python or Scala programs can also be executed like SQL Server or a Spark like big data environment. But in such case you may require to have some technical knowledge as well, that may and may not possess by the data scientists. Moreover, if you may need to transform, load and extract datasets from your datasets to other data stores then delays may be there.

Here, for this purpose, Google has announced the beta release of BigQuery ML or Machine Learning. This release of BigQuery ML is a SQL based extension to the enterprise data warehouse service that helps the scientists to build and deploy the model of machine learning. This extension reduces the time and efforts that are required to deploy predictive analytic models and it reduces the barrier to train certain models.

In this BigQuery ML SQL extension, one SQL-style statement and six functions have been added to support the machine learning algorithms. They are:

CREATE MODEL

ML.EVALUATE

ML.ROC_CURVE

ML.PREDICT

ML.TRAINING_INFO

ML.FFEATURE_INFO

ML.WEIGHTS

The user can create the model by using CREATE statement and then generate the predictions from SELECT and ML.PREDICT function.

This extension is not a general-purpose neural network modeling tool instead it can handle ad-support only two models. One is the linear regression model and the other one is a binary logistic regression model for the classification. Multiclass logistic regression cannot be handled by the BigQuery ML, but it may be supported for the future release.

SQL Query Execution in Machine Learning

For machine learning, the data is stored and executed in a different way, not like the one that is used for normal SQL queries. For CREATE statement in this language, input variables are used predictions and the categorical variables are automatically encoded in the BigQuery ML. These variables include strings, times, dates and Booleans, apart from this, standard numeric variables like float, integer, and numeric are also standardized for the prediction. The data for query execution can be drawn from multiple data sources either for the prediction or training.

Various Queries Used in BigQuery ML

Syntax for CREATE MODEL statement:

{ CREATE MODEL | CREATE MODEL IF NOT EXISTS | CREATE OR REPLACE MODEL } Model_name [OPTIONS (mode_option_list)] [AS query_statement]

Here, in this statement, syntax model_option_list specifies model types that can be either linear or logistic regression, and training parameters are also provided with this model that may be a maximum number of iterations or learning rate strategy or early stop criteria or data split parameters. To optimize the model weight, BigQuery ML uses gradient descent.

For the same statement, a simplified syntax is provided by Google. The syntax of this CREATE MODEL is given below:

CREATE MODEL ‘newdataset.newmodel’ OPTIONS ( model_type=’linear_reg’, Is_init_learn_rate=.15, L1_reg=1, Max_iterations=5 ) AS SELECT label1, column1, column2, column3 FROM ‘newdataset.newtable’ WHERE Column4 <20

Once the model is created, you may have to evaluate this in order to use it for prediction, for this, you can directly use ML.EVALUATE function for both types of models. Here a single row output is generated that holds the metrics. ML.ROC.CURVE function can also be used to evaluate the logistic regression model. This function generates multiple rows and each row has a different threshold value for the model.

For a model that is used for the prediction, you can use ML.PREDICT function. We have given an example in which the two models are compared for the predictions that are done by its two models. Here in this query a WHERE clause can also be added with the SELECT statement that can be used to predict values for table subsets.

SELECT label, predicted_label1, predicted_label AS predicted_label2 FROM ML.PREDICT (MODEL ‘newdataset.newmodel2’, ( SELECT * REPLACE (predicated_label AS predicated_label1) FROM ML.PREDICT (MODEL ‘newdataset.newmodel1’, TABLE ‘newdataset.newtable’)))

BigQuery ML minimizes the need to move from Google BigQuery to a tool that is separated one and is used to develop and train the analytical model. Even the data analysts that are not well-versed with the technical platforms like Python, R or Scala can use the tool for machine learning.

Limitations of BigQuery ML

Like every new technology and tool, BigQuery ML also has some limitations in the initial version. BigQuery ML supports two types of models one is linear regression model that can predict the numerical values like sales forecast and the binary logistic regression model like the values that can be used for two groups e.g. customer segmentation that can be used to identify spam emails and can perform other simple classifications.

BigQuery ML is based on the batch variant of the standard model that is used in gradient descent methodology. These methodologies are used to drive machine learning algorithms rather than that are used in stochastic versions.

Final Words:

As BigQuery ML is a limited version and Google has promised to improve this. The versions that are being used to improve the performance and expand its abilities will be launched soon by the company. In its newer versions, it will support many other types of machine learning algorithms. As per company the technological performance will be increased in the nearby future and will support to other machine learning algorithms as well. The technical capabilities of the tool will broaden in the nearby future. So, keep on using the tool soon you will get a different experience.