Luckily in today's day and age, we don't always have to complete super long tasks. Instead, artificial intelligence (AI) manual tasks are quickly being automated. It's why AI is massively increasing in popularity with its main goal to allow machines, like computers, to complete tasks that humans once did, in an intelligent manner. Data-centric AI is one of the most efficient and popular types and focuses more on data instead of code. As this is gaining a lot of traction its true potential is only just being discovered.
So how can you get started with data-centric AI, what are it's main features and how does it compare to model-centric AI? Let's get into it!
This technological approach is utilised by creating an AI system with data that is high quality, meaning that any data used should be extremely capable. This is so the target AI model learns everything it can. The data used should also have systematic enhancements instead of the data being fixed. This is also a crucial part of this approach as it helps to increase the overall performance and accuracy of each AI system.
In relation to data-centric AI, the amount of data is not important, however, the quality definitely is. Both the algorithm and training model should stay intact throughout the iterative developments of the data-centric systems. It's good to bear in mind that data often changes so that better performance can be provided. So it's clear to see why this approach focuses more on data management and not software development.
Model-centric and data-centric AI are complete opposite approaches. If this seems complicated to understand then don't worry as we'll break it down by comparing the two approaches. The data-centric approach focuses more so on code, however, the latter has more of a focus on the data that is needed throughout the development stages of these systems.
The model-centric approach aims to optimise each model, on the other hand the data-centric approach aims to ensure data is optimised. When it comes to the model-centric AI, inconsistency within the data may occur due to its main focus being the code. However, in terms of data-centric AI, all of the data needs to be consistent during the whole of the AI systems lifecycle.
Fixed data works very well with the former approach, fixed code works more appropriately with the latter. In short: the data-centric approach is more suitable for bettering the data iteratively, whereas the model-centric approach helps to iteratively improve the model.
Data-centric AI is a complex combination of several components, no matter how interesting it may sound it takes a lot of work for this approach to be possible. What are the components?
It's evident that when comparing each step of the data-centric AI approach, only the final two link to the “model”, however all of the other components focus on the “data”. The AI system can also be cross-checked by a professional with domain expertise when you have all of the components to get a broader look at the picture.
This is a difficult question to answer and there's no right or wrong. Although, if we take a closer look at the benefits of the data-centric AI approach, then we might be more inclined to choose this option. This is because it boosts collaboration by ensuring that different teams can simultaneously work on the system. As the focus is on high-quality data, the performance massively improves too.
Plus there's less development time, as within the training dataset there are iterative improvements, meaning the overall performance of such systems is often better. This approach helps to mitigate any issues that may occur during the deployment of the AI systems. With this in mind, it's clear that the data-centric approach is advantageous over the traditional model-centric approach.
Hopefully now you've got a better understanding of the data-centric AI components and it's hopefully given you a clearer picture on how to begin this approach yourself and reap the benefits in your career. When done correctly, the data-centric AI approach can greatly improve AI systems' performance by boosting the data quality needed to train each AI model. The future of data-centric AI is bright within a range of industries as long as data is strong.
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