Build Statistics and Machine Learning Models Using SQL in Data Distiller

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Overview

Data Distiller users need a convenient way to generate data insights to predict the best strategies for targeting users across various use cases. They want the ability to predict a user's likelihood of buying a specific product, estimate the quantity they may purchase, and identify which products are most likely to be bought. Currently, there is no option to leverage machine learning algorithms directly through SQL to produce predictive insights from the data.

With the introduction of statistical functions such as CREATE MODEL, MODEL_EVALUATE, and MODEL_PREDICT, Data Distiller users will gain the capability to create predictive insights from data stored in the lake. This three-step querying process enables them to easily generate actionable insights from their data.

Augmenting Fully Featured Machine Learning Platform Use Cases

Data Distiller's statistics and ML capabilities can play a crucial role in augmenting full-scale ML platforms like Databricks, Google Cloud AI Platform, Azure Machine Learning and Amazon SageMaker, providing valuable support for the end-to-end machine learning workflow. Here's how these features could be leveraged:

Prototyping and Rapid Experimentation

• Quick Prototyping: The ability to use SQL-based ML models and transformations allows data scientists and engineers to quickly prototype models and test different features without setting up complex ML pipelines. This rapid iteration is particularly valuable in the early stages of feature engineering and model development.
• Feature Validation: By experimenting with various feature transformations and basic models within Data Distiller, users can validate the quality and impact of different features. This ensures that only the most relevant features are sent for training in full-scale ML platforms, thereby optimizing model performance.

Preprocessing and Feature Engineering

• Efficient Feature Processing: Data Distiller's built-in transformers (e.g., vector assemblers, scalers, and encoders) can be used for feature engineering and data preprocessing steps. This enables seamless integration with platforms by preparing the data in a format that is ready for advanced model training.
• Automated Feature Selection: With basic statistical and machine learning capabilities, Data Distiller can help automate feature selection by running simple models to identify the most predictive features before moving to a full-scale ML environment.

Reducing Development Time and Cost

• Cost-Effective Experimentation: By using Data Distiller to conduct initial model experiments and transformations, teams can avoid the high costs associated with running large-scale ML jobs on platforms. This is particularly useful when working with large datasets or conducting frequent iterations.
• Integrated Workflow: Once features and models are validated in Data Distiller, the results can be easily transferred to the machine learning platform for full-scale training. This integrated approach streamlines the development process, reducing the time needed for data preparation and experimentation.

Use Case Scenarios

• Feature Prototyping: Data Distiller can serve as a testing ground for new features and transformations. For example, users can build basic predictive models or clustering algorithms to understand the potential of different features before moving to more complex models on Databricks or SageMaker.
• Model Evaluation and Validation: Basic model evaluation (e.g., classification accuracy, regression metrics) within Data Distiller can help identify promising feature sets. These insights can guide further tuning and training in full-scale ML environments, reducing the need for costly experiments.

Best Practices for Integration

• Modular Approach: Design Data Distiller processes to produce well-defined outputs that can be easily integrated into downstream ML workflows. For instance, transformed features and initial model insights can be exported as data artifacts for further training.
• Continuous Learning Loop: Use the insights from Data Distiller to inform feature engineering strategies. This iterative loop ensures that the models trained on full-scale platforms are built on well-curated and optimized data.

Advanced Statistics & Machine Learning Functions in Data Distiller

Data Distiller supports various advanced statistics and machine learning operations through SQL commands, enabling users to:

• Create models
• Evaluate models
• Make predictions

The steps above describe the following:

• Source Data: The process begins with the available source data, which serves as the input for training the machine learning model.
• CREATE MODEL Using Training Data: A predictive model is created using the training data. This step involves selecting the appropriate machine learning algorithm and training it to learn patterns from the data.
• MODEL_EVALUATE to Check the Accuracy of the Model: The trained model is then evaluated to measure its accuracy and ensure it performs well on unseen data. This step helps validate the model's effectiveness.
• MODEL_PREDICT to Make Predictions on New Data: Once the model's accuracy is verified, it is used to make predictions on new, unseen data, generating predictive insights.
• Output Prediction Data: Finally, the predictions are outputted, providing actionable insights based on the processed data.

Supported Advanced Statistics & Machine Learning Algorithms

Regression (Supervised)

• Linear Regression: Fits a linear relationship between features and a target variable.
• Decision Tree Regression: Uses a tree structure to model and predict continuous values.
• Random Forest Regression: An ensemble of decision trees that predicts the average output.
• Gradient Boosted Tree Regression: Uses an ensemble of trees to minimize prediction error iteratively.
• Generalized Linear Regression: Extends linear regression to model non-normal target distributions.
• Isotonic Regression: Fits a non-decreasing or non-increasing sequence to the data.
• Survival Regression: Models time-to-event data based on the Weibull distribution.
• Factorization Machines Regression: Models interactions between features, making it suitable for sparse datasets and high-dimensional data.

Classification (Supervised)

• Logistic Regression: Predicts probabilities for binary or multiclass classification problems.
• Decision Tree Classifier: Uses a tree structure to classify data into distinct categories.
• Random Forest Classifier: An ensemble of decision trees that classifies data based on majority voting.
• Naive Bayes Classifier: Uses Bayes' theorem with strong independence assumptions between features.
• Factorization Machines Classifier: Models interactions between features for classification, making it suitable for sparse and high-dimensional data.
• Linear Support Vector Classifier (LinearSVC): Constructs a hyperplane for binary classification tasks, maximizing the margin between classes.
• Multilayer Perceptron Classifier: A neural network classifier with multiple layers for mapping inputs to outputs using an activation function.

Unsupervised

• K-Means: Partitions data into k clusters based on distance to cluster centroids.
• Bisecting K-Means: Uses a hierarchical divisive approach for clustering.
• Gaussian Mixture: Models data as a mixture of multiple Gaussian distributions.
• Latent Dirichlet Allocation (LDA): Identifies topics in a collection of text documents.

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