Power of AI-Driven Data Analytics: BQML

Combining AI with Analytics is key to success for today's advertisers.


In today's world, where innovation is key, having data is extremely important. This means it's crucial to combine artificial intelligence (AI) with analytics seamlessly. Google BigQuery Machine Learning is a big deal in this field. It holds a vast amount of data and lets people create machine-learning models right in the data warehouse. It's an extension of Google's BigQuery, and it makes machine learning simpler by letting users use SQL commands. This means you don't have to switch between different tools.
In this blog, we'll be looking at Google BigQuery Machine Learning (BQML). This guide gives you a complete overview, covering the basics of BQML, how it works with data warehousing, what you need to get started, the different models you can use, and even a real-life example of how it can be used to sort customers for a store.

What is BQML?

BigQuery Machine Learning (BQML) helps you train and use machine learning models right in BigQuery. It's part of Google's BigQuery and makes machine learning easier. You can create models using SQL commands, so you don't need to use different tools or languages. Before starting with BQML, make sure you have BigQuery data ready. BQML works smoothly within the BigQuery ecosystem, so you don't have to move your data around or connect to other platforms. This simplifies your data analysis and storage, making everything easier to manage.

Advantages of BQML

  • You don't have to read your data into local memory
  • You don't have to use multiple languages.
  • You can serve your model immediately after it's trained.

Prerequisites Checklist

Google Cloud Platform (GCP) Account:

  • Start by signing up for a GCP account and create a project to house your BigQuery activities.

BigQuery API Enabled:

  • Within your GCP project settings, activate the BigQuery API. Simply navigate to the API Library and search for “BigQuery API” to enable it.

Step-by-Step Journey

In the first step, you take a close look at your data, like examining pieces of a puzzle. BigQuery acts as your tool, allowing you to see the structure, connections, and patterns within your data. It's like solving a puzzle, figuring out how all the pieces fit together, including the edges and centerpieces.

1. Initial phase

A.) Exploring Your Data: Think of this as taking a good look at your puzzle pieces. BigQuery helps you understand what your data looks like and how the pieces fit together.

B.) Tidying Up: Firstly,  you'll need to tidy your data. Fixing missing bits, and weird things such as data entry errors, missing values, unusual formats or symbols, and making sure everything's in order is crucial. The below image shows the BQ console for adding the data set.

Image source: https://codelabs.developers.google.com/codelabs/bqml-intro#2

2. Building Your Model:
Now that we explored and fixed our data to make sure the results are clear and expected, let's pick what model we want to use. For this, we need to accomplish a set of foundational elements written below:  

A.) Choosing Your Model: Selecting the appropriate tool for a task is analogous. Several tools (models) are available in BigQuery ML, including a number-guessing model, a sorting model, and more. Model selection involves understanding the tradeoff between model complexity and its ability to capture patterns. A more complex model might fit training data better but could overfit, while simpler models might underfit.
B.) Training Your Model: Think of this as showing your model examples of what it needs to learn. Using simple commands, you'll teach your model what to look for.

3.  Checking Your Model's Skills:

In this step, the model's performance is assessed, along with its capacity to handle scenarios outside the training dataset and its ability to make accurate predictions. We can learn more about the model's predictive skills by exposing it to unseen data. This way, we can make sure the model understands the underlying patterns in the training data and is able to generate accurate predictions or classifications in real-world situations.

A.) Testing phase: Before trusting your model completely, you will want to check how good it is. BigQuery ML helps you check if it can predict new things it hasn't seen before.
B.) Making Sure It's Right: It's like checking your answers in a quiz. You'll want to make sure your model is good at predicting the right things.

4. Putting Your Model to Work:
Putting your model to work signifies the transition from theoretical development to practical implementation. It involves integrating the model into workflows or systems, allowing it to ingest new data, generate predictions, or provide valuable insights in real-time. Model operationalization turns it into a real-world solution for predictions, categorization, and automation. Smooth integration into existing systems is vital, ensuring efficient operation. Continuous monitoring maintains effectiveness in changing contexts.

A.) Using Predictions: Once your model is ready, it's time to put it to use. With simple commands, you can ask your model to predict new things based on what it learned.

BigQuery ML offers a variety of models, and choosing the right one is like following a decision tree to the most suitable option.

Image source: https://cloud.google.com/bigquery/docs/bqml-introduction

Available Models in BQML

A model in BigQuery ML represents what an ML system has learned from training data. The many model types that BigQuery ML offers are detailed in the following sections.

Linear Regression:

Linear regression analysis is used to predict the value of a variable based on the value of another variable. It is a fundamental statistical technique, that is utilized in BQML for predictive analytics. It aims to establish a linear relationship between a dependent variable (the target to be predicted) and one or more independent variables (features). It will be helpful for:

1.) Forecasting: Predicting numerical outcomes, such as sales figures, based on historical data.
2.) Understanding Relationships: Analyzing how changes in one variable impact another, exploring correlations within the data.

  • Usage: Forecasts numerical values like sales for an item on specific days.
  • Labels: Real-valued labels, can't be infinity or NaN.

Logistic Regression:

Unlike linear regression, logistic regression in BQML is tailored for classification tasks. It predicts the probability of a binary outcome or multiple categorical outcomes. It can be leveraged in:

1. Binary Classification: Predicting outcomes with two classes, such as yes/no, pass/fail, etc.
2. Multi-Class Classification: Predicting outcomes with more than two classes, like low/medium/high, or categorizing items into different categories.

  • Usage: Classified into two or more categories (e.g., low, medium, high-value).
  • Labels: Up to 50 unique values allowed.

K-means Clustering:

K-means clustering in BigQuery ML (BQML) is an unsupervised machine learning technique used for data segmentation and pattern identification.

1. Customer Segmentation: Grouping customers based on their purchasing behavior or demographics.

2. Image Segmentation: Partitioning images into regions of similar characteristics.

  • Usage: Segments data, such as identifying customer groups.
  • Training: Unsupervised learning, doesn't need labeled data for training

Matrix Factorization:

Matrix factorization in BigQuery ML (BQML) is used primarily for collaborative filtering and recommendation systems. It can be useful in:

1. Product Recommendations: Predicting user preferences for products or items in e-commerce platforms.

2. Content Personalization: Tailoring content recommendations based on user behavior

  • Usage: Builds recommendation systems based on customer behavior and ratings.
  • Creates personalized customer experiences.

A Real Example: Sorting Customers for a Store

Let's say you run a shop. You want to know which customers buy similar things so you can offer them better deals. Here's how BigQuery ML can help:

What to do:

  • Goal: Sort customers into groups based on what they buy.
  • Using Data: Take past sales data with what each customer bought and how often.
  • BigQuery ML: With simple steps, you can group customers who buy similar things.
  • Checking the Groups: Make sure these groups make sense for offering special deals or services.
  • Using the Info: Now that you have these groups, you can tailor your offers for each group to make customers happier.


  • Choosing Wisely: Pick the most important things from your data that will help your model.
  • Learning and Tweaking: Do not if your model isn't perfect the first time. Keep teaching it new things and making it better.
  • No Limits: BigQuery ML handles big amounts of data without slowing down, so you can keep using it as your data grows.

In Conclusion

BigQuery ML is like a superhero sidekick, making machine learning easier for everyone. It helps businesses make better choices using their data, improving how they work, serve customers, and make decisions.






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