The internet, and online processes, are getting smarter. Big data is becoming so vast that it now understands your decisions better than you do. The mysterious and all-encompassing algorithm is quickly removing any residual traces of agency we believed remained. In short, tech is racing ahead and leaving those who won’t, or can’t, use it in the dust. (1)
One example of this kind of pioneering technology is BigQuery ML. The new Google feature is set to make waves in the field of predictive technologies. In this article, we look at how to use BigQuery ML to predict website content for visitors.
What Exactly is BigQuery ML?
Google is a company that’s had a few product and system rollouts that leave some things unexplained. In this instance, the new BigQuery ML is no exception. The ML stands for “Machine Learning,” and is symbolic of just how advanced the platform has become.
BigQuery ML is in fact an upgrade or additional feature of the established, Google BigQuery. It’s comprised of extensions for SQL, necessary for the development of models that are able to engage in machine learning. This can then evaluate predictive performances which can be populated in BigQuery.
The ability to integrate your data with BigQuery is changing the game in a few ways. BigQuery ML only requires the standard SQL language, which ultimately increases the accessibility of an advanced technology like machine learning.
It provides ample opportunity to save time compared to previous systems, and it reduces overall training time. This is because BigQuery ML works within BigQuery, meaning there is no need for the tiresome and time-consuming process of exporting the data for use elsewhere.
Below, we look at how to predict website content for visitors.
Begin by creating a dataset
This step is optional, although it’s generally a good idea to start from the ground up when familiarizing yourself with a new platform. Begin by opening a table and saving it in a data set. There are two steps needed to do this:
- Using the BigQuery interface, find the project you’re working on click to create a dataset.
- Give a newly created data set a name, location, and expiration.
Make the model
In the BigQuery ML platform, you need to create models that will capture underlying patterns within the data. Predicting the outcomes of these patterns in real life events that include unseen data is going to be the basis of your website content prediction.
BigQuery ML supports three different kinds of models (all called “x regression”):
- The first is linear. This predicts continuous numeric variables, such as age or income.
- After this comes “binary logistic.” When categorical variables hold two possible outcomes, this attempts to make the prediction as best as possible with the available data. An example of a binary outcome includes the possibility of buying, or not buying, from a website.
- The last is “multinomial logistic.” Sometimes referred to as “multiclass,” this predicts the outcomes of a variable that has a greater number of possibilities than two.
After creating the kinds of models listed above, it is necessary to then specify:
- The name and save location of the model.
- A list of model options (model_option_list) where model related options are fed into the system to develop the training process.
- What query would then generate a table (query_statement). This will populate training data.
In model and algorithmic systems, we need to find the model information to determine the relationship between features and response variables (i.e., the space in between). In models that are linear, when the explanatory variable’s magnitude is great, this affects response variables, and many others.
The model needs to be evaluated
The training is the first step, but what comes next is refining and developing its predictive model. This kind of development needs to be conducted on separate test sets from where the training took place. What this does is removes the likelihood of overfitting, which is the result of models memorizing patterns of training data so well that its precision hinders its ability to make predictions. Functions that can be used to evaluate models include:
- The ML.TRAINING_INFO. function identifies different versions during the model training. This includes loss when conducting the training, so then in the validation of each version.
- Another is the ML.EVALUATE. function which is able to provide the most likely metrics to test the model’s predictive performance.
- To assess a data set’s confusion matrix, use ML.CONFUSION_MATRIX. By doing this, you can troubleshoot errors in each classification area.
- The final is ML.ROC_CURVE. With this function we can assess what is commonly referred to as an ROC curve. This is a function used to assess the predictive capacity on a double (binary) classification model. It does this visually through graphical analysis, and is a favorite of BigQuery ML users.
After your toils, the making predictions is the reward. This feature of BigQuery ML has been a long time coming and is the reason for a lot of the hype behind this product. The democratization of these predictive capacities is the culmination of years of technical analysis and research that began with BigQuery’s revolutionary introduction in the 2010s.
To get started with this process, you will need to make a new model. By doing this, you are creating a model that makes predictions based on an established data set. This query will include input tables and the resultant model predictions. For logistic regression models, this will estimate the probability of an outcome, regardless of whether it is a binary or multiclass model.
Now that you understand the technical details, it’s time for implementation. These predictions can provide appropriate website content for visitors, which in turn drives engagement, and potentially sales, on your platform. You can decide which channels are best for specific viewers, giving you the ability to cater and personalize the experience of your website’s visitors. With this powerful, and continually improving technology at hand, your potential for digital growth and market share can be fully realized. (2)
- “How AI Makes Big Data Smarter,” Source: https://www.forbes.com/sites/forbestechcouncil/2020/03/23/how-ai-makes-big-data-smarter/?sh=a5985c846849
- “How Predictive Analytics Can Help Your Business,” Source: https://medium.com/trapica/how-predictive-analysis-can-help-your-business-5b24376089a8
Author: Pablo Sergio is a Technical Content Writer at Coupler.io, a solution for integrating data between various sources and destinations on a schedule. Writing is Pablo’s passion, and he has 5+ years of experience in creating meaningful content for data enthusiasts.