Predictive analytics is transforming industries by enabling data-driven decision-making. By using AI, businesses can predict trends, optimize operations, and even preempt challenges before they arise. But for a beginner, setting up an AI-driven predictive analytics project can seem daunting. Figuring out whether there is value in your data that AI can uncover, traditionally has required significant expertise in AI and data engineering, and many smaller businesses who cannot afford a data science team or don’t have the budget for a big project.
That's where Featrix steps in, streamlining the process so that developers can create sophisticated AI models without needing deep expertise in data science. In this guide, we will walk through how to start a predictive analytics project using Featrix, from preparing your data to deploying AI models and interpreting results.
Introduction to Predictive Analytics
What is Predictive Analytics?
Predictive analytics is a data-driven approach that uses historical data, statistical algorithms, and machine learning techniques to make forecasts about future outcomes. By identifying patterns and trends in past data, predictive analytics models assess the likelihood of specific events, such as customer behavior changes, equipment failures, or market shifts. Unlike descriptive analytics, which summarizes past data, predictive analytics focuses on delivering actionable insights that help organizations anticipate and respond to upcoming challenges and opportunities.
| Step # | Step Name | What's Involved? |
| 1 | Learn Data Science concepts | Algorithms, test vs training data, features, hyperparameters, classification vs. regression, model types |
| 2 | Gather and Prepare Data | Join data from multiple sources, clean and preprocess, perform EDA (Exploratory Data Analysis) |
| 3 | Extract Features | Identify performant representations of input |
| 4 | Train Model | Select type of model, split data into train vs test, train initial model |
| 5 | Evaluate and Optimize Model | Evaluate initial performance, tune hyperparameters, improve training data |
| 6 | Prepare for Deployment | Meet size and speed requirements, transform into deployable code, connect with data sources |
| 7 | Deploy and Monitor | Integrate with application, run in production, monitor for performance degradation and data drift |
Over several decades, the data science community developed what’s known as predictive analytics, and a workflow that requires a lot of iterations and some steps that are challenging even to experts - such as feature and model selection, just in case you’ve heard of them.
Don’t worry if you’re not familiar with these steps! The simplified workflow we’ll describe next doesn’t require them.
Embedding Approach to Predictive Analytics
Building predictive models is possible without deep experience in data science or AI, employing a simplified process that’s possible with what we call the embedding approach to predictive analytics that we’ll dive into next.
How does the “embedding approach” to predictive analytics work? Vector embeddings, originally used for unstructured data in AI applications like semantic search and generative models, are now revolutionizing predictive analytics for structured data. This approach simplifies the process of creating predictive models by transforming structured data into an embedding space. Unlike traditional machine learning methods that require extensive data cleaning, preparation, and AI expertise, this embedding-based approach can work with raw data and deliver performant models with minimal effort.
The key advantage of this method is its ability to bypass the time-consuming and expertise-dependent steps of traditional predictive modeling. Where classic machine learning often demands that teams spend up to 80% of their time on data preparation and requires significant AI knowledge for model optimization, the embedding approach compresses the entire model development process into a few simple steps. It evaluates the inherent structure of the data and, if suitable, constructs predictors based on embeddings as "neural functions." And once you have a performant model, you can deploy it using simple API calls, significantly lowering the barriers to entry for predictive analytics across various industries and applications.
Using the embedding approach, getting from raw data to a performant model proceeds in just three steps.
Simplified Predictive Modeling with Featrix
In this section you get a high-level overview of the steps, and hints for actually performing them in Featrix. Refer to our documentation for up-to-date specific instructions on the Featrix UI and API.
Step 1: Problem and Data Exploration
As you get going with any AI project, you need to define what prediction needs to accomplish in the context of the problem you want to solve. Predictive analytics typically answers questions like:
- Forecasting: "What will happen in the future based on past trends?"
- Classification: “What condition is this process in? Working normally, paused, unstable, etc.”
- Regression: "Which setting would optimize operations based on current state?"
Here are the three steps how Featrix gets you from data upload to understanding relationships:
| Sub-Step # | Step Name | What options you have / How to trigger step |
| 1-1 | Upload your dataset | Upload a file (csv or JSON) from you local computer or using the Featrix API, connect a MongoDB, or read data from a URL. |
| 1-2 | Kick off creation of the Featrix embedding space | Click "Create Embedding Space”. To save time on an initial run, select the “Super Quick” option. |
| 1-3 | Inspect data and quality of embedding space | The "Data Sources" tab provides preview of your data, like histograms for each column. The "Embedding Explorer" lets you investigate the embedding space and its properties. |
Step 2: Problem and Data Exploration
Once your data is prepared, the next step is to build a model that learns from historical data to make predictions. Traditionally, this requires selecting an algorithm and model type, tuning hyper parameters, and evaluating performance - complex steps for a beginner, because you would have to choose among a dozen different model types, interpreting evaluation metrics, while hyper parameter tuning is typically performed automatically these days.
Not with Featrix, once you have an embedding space, all you have to do is kick off training (in the UI, click the “Create Predictive Function” button on the "Neural Functions" tab).
Building a model is only part of the process. Evaluating how well it performs is critical to ensuring that your predictions are reliable and actionable. Considerations you’ll have to work through include:
- Choose appropriate metrics to evaluate performance (e.g., RMSE for regression, accuracy/precision/recall for classification)
- Establish a set of test data that accurately reflects model operation in production - which is really challenging! You probably have to gather data while operating your model.
Featrix includes built-in evaluation metrics and visualizations, allowing you to assess and interpret model performance. The screenshot below shows accuracy, AUC, and F1 score trends across predictor training epochs.
Step 3: Deployment and Monitoring
After building and evaluating your model, the final step is deployment. You'll want to use your AI model to make real-world predictions on new data, whether it's integrated into an app or a standalone tool. That typically involves the following tasks:
- Integrate the best model with your application
- Deploy model and application in a production environment
- Continuously monitor its performance and optimize as needed
Featrix provides easy deployment options, including APIs for developers to integrate AI predictions directly into their applications. Whether you're deploying in the cloud or on-premises, Featrix ensures your models are ready for production quickly. And retraining your model with additional data is easy.
Start your predictive analytics journey today with Featrix, and see how easy AI-driven insights can transform your business!
What's next?
How do we recommend you actually get started? If you don't have a clear vision how predictive analytics applies to your industry, you can get inspiration from our blog series on industries, find the "Industries" ones on our blog page.
Ready to dive in? Our documentation provides more detailed step-by-step instructions on key steps in the workflow, including uploading data and training a neural predictor. Check out the documentation index for information on additional topics.
