AI data modeling: Automate & accelerate predictive analytics

Artificial Intelligence is transforming the way we work with data, particularly in the area of building AI data models for predictive analytics, using AI to analyze data, and making data-driven business decisions. Data quality must be prioritized to ensure data models yield accurate results, as bad or stale data leads to bad results.

Data engineers spend hours creating models using traditional methods, which include processes like cleaning, analysis, and manually selecting variables and assumptions. As data grows in size and complexity, the challenge extends beyond time consumption; maintaining data quality also becomes increasingly difficult and error-prone.

Compared to traditional methods of building data models, AI data modeling automates key processes, leading to more accurate and impactful analytics. Leveraging AI for data modeling not only means these processes are automated, but also means technical users, citizen developers, and non-technical users can build deployable and trustworthy models without writing complex code.

In this blog post, we will discuss the key concepts of AI data modeling, the stages involved, and how AI can be used to streamline the process of creating efficient data models.

What is AI data modeling?

AI data modeling is the use of machine learning techniques and automation to analyze structured data, identify patterns, and build predictive or descriptive models. When processes like data preparation, schema design, and feature engineering are automated, users can build models faster and more efficiently.

Traditional methods of data modeling can be time-consuming and are difficult to scale and adapt to new data, especially when dealing with large datasets. AI data modeling solves a bulk of these challenges and offers additional benefits. Let’s explore some of these benefits.

Benefits of AI data modeling

• Speed: The major advantage of AI data modeling is its speed, especially when compared with traditional methods. With the automation of repetitive tasks, users can build models faster with minimal manual intervention.
• Scalability: AI data modeling tools can handle large datasets, so users do not have to worry about performance bottlenecks when working with large amounts of data.
• Accessibility: AI data modeling strategy bridges the gap between data literacy and data fluency as it allows users of varying skill sets to build models without writing complex code. This is especially important in promoting a data-driven culture in organizations as users can self-serve analytics.
• Accuracy: Compared to manual methods that rely on selecting variables and making assumptions, AI data modeling uses advanced data modeling techniques for improved predictions, allowing users to make more informed decisions.

Data preparation

The first stage in building effective models through AI data modelling is to prepare your data. This means cleaning your data and transforming it into a form that machine learning algorithms can easily understand. Data cleaning simply means ensuring your data is free from inconsistencies like missing values, duplicates, inconsistent formatting, and outliers. Data transformation, on the other hand, involves processes like normalization, standardization, and encoding.

Cleaning data could take either minutes or hours, depending on your approach. Manual cleaning involves the use of spreadsheets like Excel and Google Sheets, which takes a ton of time and is still prone to human errors. Scripted cleaning involves the use of programming languages like Python to implement automation. While this method saves time, it requires a level of technical expertise. Users can also utilize dedicated data cleaning tools to clean data faster.

Similarly, manually transforming data into a suitable form can be difficult. Data has to go through the normalization process, which entails bringing all the feature values in a dataset into the same range (either 0 or 1). This is important when working with different units or scales, as models may be biased towards the higher range. For example, Age values would typically range from 18-80, while Income values could range from $20,000 - $500,000. Without normalization, the model would be biased towards the Income value.

Data should also be standardized and follow a predefined format for names, dates, and locations. For example, NYC, New York, and New York City all refer to the same location but will be parsed differently by machine learning algorithms, which would lead to incorrect results. Therefore, data must be properly scrutinized in the preparation process to ensure accurate results and the reliability of predictions.

Leveraging tools like Quadratic in data preparation allows you automate cleaning and transformation without coding expertise. Quadratic’s built-in AI empowers users to clean large datasets with ease and automatically transform data into a consistent format suitable for machine learning algorithms.

Feature engineering

Feature engineering is one of the most time-consuming stages in data modeling. It involves the creation and selection of features in a dataset for improved model accuracy. Data models perform faster when they’re trained on meaningful features, so data engineers are tasked with creating and selecting only relevant features from a dataset. Rather than manually creating features based on domain knowledge, AI data modeling provides the most predictive features in a dataset, reducing noise and overfitting. It automatically selects features using techniques such as Recursive Feature Elimination (RFE), Principal Component Analysis (PCA), and L1 regularization.

Tools like H2o.ai utilize AutoML to automatically rank and test tools based on their relevance to the model’s performance. Quadratic’s built-in AI also allows users to access important features following instructions from text prompts. For example, you can simply ask “Create a feature that calculates customer tenure in months” it instantly creates a new feature based on the prompt.

Model selection and training

This stage involves choosing the most appropriate algorithm to make predictions and learn patterns from data.

Common algorithms include linear regression, decision trees, and neural networks. It’s difficult to predict the best algorithm to use, so data engineers use trial-and-error to compare different models. To get a better fit, you’d have to tune hyperparameters such as learning rate, max depth, and number of layers. After doing this, you evaluate the model’s performance using validation metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE).

Tools like Google Vertex AI and DataRobot help to automate the stages involved in model selection and training. Data engineers won’t need to manually test each algorithm to know the best fit as these tools automatically evaluate your dataset and suggest the most suitable algorithm to use. These tools provide automation in tuning hyperparameters to each algorithm for maximum performance. Validation is also streamlined as processes like data splits, cross-validation loops, and model comparison are seamlessly handled, ensuring optimal performance without manual code setup.

Natural language querying

AI natural language querying allows users to ask questions about their data or model using plain text prompts. This eases data exploration and aids better understanding of the dataset without needing to write code or navigate complex BI tools. Data engineers can leverage natural language queries to automate processes like data cleaning, feature engineering, model tuning, and bias checks. This approach allows users to gain faster insights from their data and also promotes data democratization and accessibility in organizations.

Platforms like Quadratic offer robust AI querying capabilities with additional features like collaboration, support for spreadsheet coding, and direct connection to multiple data sources.

Validation and bias checks

Validation is a crucial step in AI data modeling. It’s a measure of how well a model performs on unseen data, which is an actual test of the model’s accuracy. To avoid overfitting (a situation where a model matches too closely to the training set and fails to make predictions on new data), the model must be validated against a test set. Ideally, about 20-30% of the data is used for testing, and the remaining 70-80% for training. Validation ensures consistent performance across different data subsets.

Models must also be checked for bias to ensure fairness across different groups. Bias in models can be detected using techniques like group performance metrics (evaluating how the model performs across different groups, fairness metrics (using specialized metrics such as disparate impact and demographic parity), or the use of bias detection tools.

Modern AI tools like H20.ai and Fairlearn streamline validation and bias checks by automatically performing cross-validation during model training, breaking down performance by subgroups, and seamlessly flagging biased outcomes.

Deployment

Once the model has been validated, the next step is to deploy it so it can be used in real-world instances. Let’s explore the common model deployment options:

• Batch deployment: This method serves data models on schedule or in batches. It is particularly useful for large datasets and in instances where users want access to their model consistently but not in real-time.
• Real-time deployment: Real-time deployment is best suited for scenarios that require predictions from models in real-time. This method involves deploying models as APIs so predictions are returned instantly.
• Spreadsheet deployment: Tools like Excel, Google Sheets, and Quadratic allow users to access their models in a spreadsheet, although Excel and Google Sheets require integration with third-party tools like Azure ML and BigQuery ML.

Explainable AI

Explainable AI (XAI) refers to a set of techniques and processes that enable humans to understand, interpret, and trust the decisions made by machine learning models. It provides transparency into how a model arrives at a specific prediction or result.

This is especially important in scenarios where AI models influence high-stakes decisions. Users must be confident that a model is fair and capable of delivering accurate results. Techniques for implementing explanability include:

• SHAP: This stands for SHapley Additive exPlanations. It explains the prediction of a model by breaking down individual feature contributions(such as age, income, and debt) using game theory. Users can get insights into the most important features and how much they affect the model’s output. It works for all types of data models and can be implemented using the SHAP library in Python.
• LIME: This stands for Local Interpretable Model-agnostic Explanations. LIME provides an explanation for individual instances of a model’s predictions. It is relatively simpler to use as it explains only a small part of the ML function. It can be implemented using the LIME package in Python.
• Partial Dependence Plots (PDP): This visualizes the marginal effect one or two features have on the output of the model. It gives insight into how predictions change with one feature while keeping the other constant.

To build the trust and confidence of users in a model’s output, explainable models must be used in all applicable areas. AI tools can help in explainability by translating model decisions into plain language summaries based on users’ queries. This makes insights accessible to data engineers and other stakeholders without technical expertise.

Real-world applications of AI data modeling

Areas where AI data models can be used include:

• Marketing: Customer churn prediction, campaign optimizations, and personalized recommendations.
• Finance: Financial forecasting, risk assessment (such as FMEA analysis), and fraud detection.
• Healthcare: Disease prediction, drug discovery, and medical imaging analysis.
• Operations: Route optimization, inventory tracking and optimization, and demand forecasting.

Conclusion

Creating high-accuracy data models can be both time-consuming and complex, especially when each step must be handled manually. AI data modeling simplifies and accelerates this process, automating key stages like data preparation, feature engineering, and model training. This enables users to build efficient and accurate models with far less effort and expertise.

In this blog post, we explored AI data modeling concepts, their advantages over traditional methods, and the typical workflow involved in building a model. We also discussed how AI-powered tools like Quadratic automate these steps, offering robust AI spreadsheet analysis and making advanced data modeling accessible to users of varying skill sets.

If you’d like to explore AI data modeling in Quadratic, this machine learning tutorial template is a great place to start.