As Artificial Intelligence is emerging, there is no doubt that it will transform businesses and organizations faster than ever before. But a fact that should not be ignored is that with the rise in AI, the data volumes are also growing along with its increasing complexity. Amid this, AI and ML are the significant technologies that can help data scientists filter the data into relevant value.
From training models to input-for-insights, data lies in the heart and soul of AI solutions. Even for enterprise AI, the collection of data is a continuous process that compels AI projects to operate on modernized data collection and curation strategy. Therefore, organizations and enterprises need to focus more on AI data infrastructure for the smooth and swift functioning of their data-driven and AI-enabled processes.
Here are some significant attributes of an appropriate AI data infrastructure.
The infrastructure should have extensible metadata, where metadata refers to ‘data for data’. There are two types of metadata – one that is system generated and second that are defined by the user. The data-tags used in the metadata should reflect some significant mentions including the name of a project, the source of data, whether data contains personally identifiable information or a practically infinite variety of characteristics derived from the data itself.
Also, an efficient data infrastructure should support system-generated metadata that has been sourced from different places – object stores, file systems, cloud repositories, to name a few. It should also support user-defined metadata. Moreover, the AI data infrastructure should possess the capability to provide mechanisms that can make these tags accessible to higher-level ML frameworks while not stressing over the underlying storage technology.
Considering tags as a significant feature in this process, it is necessary to acknowledge solutions that can reduce the effort associated with tagging data and save time as well. Ideally, the effective data infrastructure should support auto-tagging which means extracting tags from existing metadata. It can also use deep inspection policies to extract text and metadata directly from raw data files using various tools.
However, a data extraction tool can be a pre-trained model. It may be a program that classifies images or interprets customer sentiment from different styles of correspondence.
Moreover, as data comes in different forms, the AI data infrastructure should be flexible enough to allow multi-protocol data access. This accessibility will significantly curb expensive and inefficient data duplication, and increase the execution of data pipelines.
Additionally, to assist varied protocols, the AI data infrastructure should support auto-tiering and multi-temperature storage which means data can reside on a hot storage tier when it belongs to active projects and can be transferred in cooler storage tier when shifts to less frequently accessed projects. Further, for efficient and effective AI-enabled data infrastructure, scale and performance are considered a critical aspect too.
Hence, from data pipeline, data ingest and edge analytics, to data prep and training in the core data center, to storing it an appropriate place, designing a data infrastructure suitable for AI requires a holistic approach. To understand the performance requirements and data service needs is extremely critical to develop an AI data infrastructure.