In this post, we explore how we can take a business problem to potential business outcomes using Adobe Experience Platform Data Science Workspace to quickly connect to data and build, experiment, validate and deploy machine learning models at scale.
While the model building itself has several steps like data cleaning, data preparation, feature engineering, and ultimately, model building, Adobe Experience Platform provides a much broader and wider end-to-end framework to accelerate the data-to-insights process, perform experiments through hyperparameter tuning, and creating a custom, personalized and intelligent publishing service which can be shared in just a few clicks. This service can be monitored, and retrained for continuous optimization of personalized experiences
The end-to-end machine learning framework in Adobe Experience Platform can be broken down into five interdependent steps.
Machine Learning Framework in Adobe Experience Platform
The first step is learning from data. For this, we build a machine learning model to find patterns and insights within our collected data. We can think of our model as a set of instructions. What makes a machine learning algorithm different is that instead of having a set of instructions, we start with the data and the final outcome. The machine-learning algorithm then looks back at the data and works out the set of instructions to reach the final outcome in the most optimized way.