A day is coming when everyone will have to leverage machine learning (ML) to stay competitive. But traditional approaches to machine learning development and production are thwarting the ML efforts of many. Traditional approaches are slow, code-heavy, and repetitious – they are devoid of self-service.
In this ebook, we highlight the areas of the machine learning lifecycle where the need for self-service is the most acute. We expose the flaws in traditional ML approaches across four key stages:
- Data acquisition: Gathering data for creating training datasets
- Data exploration and preparation: Profiling, cleansing, organizing, labeling, and transforming data in preparation for model training
- Model training and evaluation: Building, training, and cross-validating machine learning models
- Model deployment: Operationalizing machine learning models in a real-world setting
A self-service solution for ML reduces tedious hand-coding, cuts down on repeat work, and fosters collaboration between data scientists, data engineers, and IT/DevOps throughout the machine learning lifecycle. It enables you to build a greater number of high-impact models faster.
Download the full ebook, “The dawn of self-service machine learning: Fixing the flaws of traditional ML development” to learn how to accelerate your machine learning projects with self-service.