There is a lot of discussion around AI and which approach is better: model-centric or data-centric. In this blog post, we will explore both approaches and give examples of each. We will also discuss the benefits and drawbacks of each approach. By the end of this post, you will have a better understanding of both AI approaches and be able to decide which one is right for your business! As product managers and data science architects, you should be knowledgeable about both of these AI approaches so that you can make informed decisions about the products and services you build.
Model-centric approach to AI is about having focus on using right set of machine learning algorithms, programming language and AI platform to build high quality machine learning models. This approach has resulted in great advancement in the field of machine learning / deep learning algorithms. The focus on building high performing models resulted in lot of AI / machine learning / deep learning frameworks using different programming languages such as Python, R, etc. Some of these popular frameworks include Python Sklearn, Tensorflow, Pytorch, etc. Apart from that, almost all cloud service providers came up with AI / ML services focused on building machine learning models. In addition, this led to lot of professionals taking up data sciences and machine learning as their career.
The picture below represents model-centric vs data-centric AI. In model-centric AI, the focus is to get the code (model) right while in data-centric AI, the focus shifts to data as shown in the picture below.
The following are some of the focus areas of model-centric AI:
Some of the benefits of this approach are:
Data-centric approach to AI is about having focus on getting right kind of data which can be used to build high quality, high performance machine learning models. Unlike model-centric AI, the focus shifts to getting high quality data for training models rather than models. The following are some key aspects of high quality data set which can result in high quality machine learning / AI models:
Some of the benefits of data-centric AI are:
Some of the drawbacks of data-centric approach are:
Model-centric and data-centric AI are two different approaches to AI. The model-centric approach is about having focus on using right set of machine learning algorithms, programming language and AI platform to build high quality machine learning models. This approach has resulted in great advancement in the field of machine learning / deep learning algorithms. Data-centric approach to AI is about having focus on getting right kind of data which can be used to build high quality, high performance machine learning models. Unlike model-centric AI, the focus shifts to getting high quality data for training models rather than models. While there are many different ways to approach AI, we believe that a hybrid or balanced approach of adopting model-centric and data-centric model is the most effective way to create intelligent machines. We’ve outlined the benefits of booth these approached of AI and how they can be used in business. If you have any questions about our services or would like more information, please let us know. We would be happy to discuss our approach further and answer any questions you may have.
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