Data is the fuel that powers AI and ML models. Without enough high-quality, relevant data, it is impossible to train and develop accurate and effective models.
DataOps (Data Operations) in Artificial Intelligence (AI) is a set of practices and processes that aim to optimize the management and flow of data throughout the entire AI development lifecycle. The goal of DataOps is to improve the speed, quality, and reliability of data in AI systems. It is an extension of the DevOps (Development Operations) methodology, which is focused on improving the speed and reliability of software development.
What is DataOps?
DataOps (Data Operations) is an automated and process-oriented data management practice. It tracks the lifecycle of data end-to-end, providing business users with predictable data flows. DataOps accelerate the data analytics cycle by automating data management tasks.
Let's take the example of a self-driving car. To develop a self-driving car, an AI model needs to be trained on a large amount of data that includes various scenarios, such as different weather conditions, traffic patterns, and road layouts. This data is used to teach the model how to navigate the roads, make decisions, and respond to different situations. Without enough data, the model would not have been exposed to enough diverse scenarios and would not be able to perform well in real-world situations. DataOps needs high-performance and scalable data lakes, which can handle mixed workloads, and different data types audio, video, text, and data from sensors and that have the performance capabilities needed to keep the compute layer fully utilized.
https://www.tagxdata.com/the-ultimate-guide-to-data-ops-for-ai
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