According to Walid El Kara, sales engineer at Dataiku, model development isn’t just about accuracy; it's about harnessing the power of tools like Dataiku to drive adoption and create high value for end users. In a 2024 Dataiku Product Days session, “Building my First Model: Jumping Into Predictive Analytics With Visualization,” Walid demonstrated how to accomplish this value-creation goal by building a machine learning (ML) model with Dataiku. This blog highlights the key takeaways from the presentation.
Connecting the Dots With Dataiku
Noting the overwhelming number of disparate and siloed data sources in the business landscape, Walid began the session by highlighting how Dataiku, the Universal AI Platform, connects to numerous data sources. The platform's emphasis is on enabling analysts to easily gather and process information from different compute engines to drive valuable insights. Walid affirmed that it is this connectivity and adaptability that truly meets the diverse requirements of business users and makes Dataiku a valuable ally in every analytics journey.
Real-Time Demo: Predicting Customer Churn
Walid used a relatable case study to demonstrate the model-building process in Dataiku, focusing on predicting the likelihood of customer churn using web data from an e-commerce platform. The steps for building the ML model are outlined as:
- Data selection and ingestion
- Exploratory data analysis
- Feature generation
- Modeling
- Deployment
The demonstration was packed with practical tips — from choosing and connecting to your data source to shaping it for modeling, the focus was on ease and flexibility. The platform’s visualization capabilities shined throughout the demonstration, proving that not only can you easily maneuver through complex calculations, but also make informative and visually appealing reports in no time.
Feature Generation and Model Training
Walid proceeded to discuss feature generation and model training. By extracting additional context from a user's IP address and birth date, Walid illustrated how 'simple' variables could be transformed into powerful signals for the predictive models. He also demonstrated the functionality of prebuilt features in Dataiku, simplifying routine tasks like parsing dates and computing age and, ultimately, accelerating data preparation tasks.
By creating additional features like a GDP strength indicator, Walid showed that Dataiku’s power truly lies in its flexibility and speed of efficient data processing.
Automation, Prediction, and Deployment
Keeping pace with the evolving modeling landscape, Dataiku’s AutoML approach made training prediction models a breeze. As Walid explained, Dataiku supports your modeling process by offering explanations and suggestions. Whether it's for optimizing models or customizing them to accommodate specific requirements and risk appetite, Dataiku has users covered. Once the model training is complete, the platform facilitates easy publishing of the model documentation, feature importance, interpretability, and more for further analysis.
Understanding the Power of Iteration
The final thoughts of the session revolved around the importance of the iterative process in ML. Walid clarified how to evaluate and select the right model for a specific use case, and how Dataiku made it straightforward to generate, test, and iterate on features and models. For example, Dataiku gives users the ability to evaluate the model against a real-world dataset to generate predictions, and more.
Throughout this session, Walid highlighted Dataiku’s versatility and adaptability in the model building process. Whether the purpose is predictive modeling, feature selection, or an iterative process, Dataiku stands out as a powerful ally in creating high-value end results.