Data management considerations for ML

Data management is a broad and complex topic. Many organizations have dedicated data management teams and organizations to manage and govern the various aspects of a data platform. Historically, data management primarily revolved around fulfilling the requirements of transactional systems and analytics systems. However, as ML solutions gain prominence, there are now additional business and technology factors to consider when it comes to data management platforms. The advent of ML introduces new requirements and challenges that necessitate an evolution in data management practices to effectively support these advanced solutions.To understand where data management intersects with the ML workflow, let's bring back the ML life cycle, as illustrated in the following figure:

Figure 4.1: Intersection of data management and the ML life cycle

At a high level, data management intersects with the ML life cycle in three stages: data understanding and preparation...