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Databricks offers an interactive workspace for data scientists and engineers to develop, train, and serve machine learning models. One of the significant advantages of Databricks is its seamless integration with SQL and ML tools. In this tutorial, we will explore how to integrate machine learning models using SQL in Databricks.
Introduction to SQL and Machine Learning in Databricks
Databricks allows users to query data using SQL directly, providing a robust platform for running ad hoc queries and creating reports. This seamless SQL integration makes it much easier to prepare and analyze data for machine learning models.
Creating a Table
Initially, we need to have data available for the Machine Learning model training. For this purpose, we might create a table:
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CREATE TABLE Student_Scores ( student_id int, exam1 int, exam2 int, final int ); |
Training a Machine Learning Model
Once the data is ready, we can train an ML model using the data. We will use a linear regression model as an example:
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-- Import necessary libraries %python from pyspark.ml.regression import LinearRegression -- Load training data training = spark.read.format("libsvm").load("data/mllib/sample_linear_regression_data.txt") -- Initialize linear regression lr = LinearRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8) -- Fit the model lrModel = lr.fit(training) |
Deploying a Machine Learning model
After training the model, the next step is to deploy it for data predictions. Below is an example of how to use the trained model for SQL scoring:
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-- Register the model lrModel.write().overwrite().save("/tmp/lrModel") -- Use model for prediction %sql SELECT student_id, lrModel.predict(ARRAY(exam1, exam2)) as predicted_score FROM Student_Scores; |
In the above example, the model predicts the final score for each student given their scores in the two exams.
Conclusion
Databricks provides a unique platform that allows data scientists and engineers to develop, distribute and run their ML models using familiar SQL syntax. With SQL capabilities, users can seamlessly link machine learning projects, making the development process more streamlined and efficient.