Everything About Data-Centric AI

Data sets can be considered a fundamental part of the AI training and automation process. For this, the data-centric AI approach is becoming quite popular. It involves processes that systemize data and improve its quality so that the performance of the system can be improved. If you are looking for quality data sets to train your data-centric AI models then do check out Macgence. Their data sets will ensure that your AI models are optimized to their best so that accurate results can be produced.

In a data-centric approach, the quality of the data sets that are used to train an AI model is improved. In this blog, we’ll discuss in detail about data-centric AI. Keep reading, and keep learning!

What is Data-Centric AI?

The initial ways of approaching AI development involved working on codes that form the base of an AI model. However, data-centric AI aims to improve the quality of training data. Adding more diversity to the data, cleaning it, and more can be done to accomplish this.

The code and the data are the two main parts of an AI model. To enhance the output and precision of the AI model, a data-centric AI method concentrates on the data. A model-centric strategy, on the other hand, concentrates on code optimization to improve the AI model.

The data-centric approach is a better one as it reduces the development time of the model. It was observed that companies that followed a data-centric AI approach saw around 20% improvement in the performance of their AI models as to the companies using a model-centric approach. 

Why opt for a Data-Centric Approach?

While choosing data to feed your AI model, your focus should be on the quality and not the quantity. Randomly collected data is prone to having fillers and distractions. When such data sets are used to train AI models, they are bound to produce errors in their results. So, that is the primary reason data-centric AI is required. To counter the challenges offered by a model-centric AI training approach, a data-centric approach was introduced. 

Implementation of Data-Centric AI

Following is the process that goes behind the data-centric approach: 

1. Quality data sets that have defined labels and that cover important cases are sourced by a company. They may have in-house experts to produce such data or they may get it from quality AI training data marketplaces like Macgence. 
2. Before starting the work on the entire data set, an industry expert works on a small data sample to check for inconsistent areas.
3. While this is being done, labeling instructions that have special cases are also recorded as an outcome of error analysis. 
4. Moreover, all the noises or empty cells from the data set are removed to cleanse the data. 

Benefits of Data-Centric AI

Below listed are some of the common benefits offered by data-centric AI:

1. Improves Performance: This approach involves the building of AI models with quality data so that the data itself can convey the learnings to the AI models. This results in better performance and the need for trials and errors is also eradicated. 
2. Promotes Collaboration: Following a data-centric approach will lead to better collaboration between the members of a team. In a data-centric approach, professionals can work together to identify bugs and can collectively perform further optimizations by tweaking the data sets. 
3. Reduces Development Time: The major advantage of data-centric AI is that it reduces the time required for launching an AI model into the market. Teams can work parallelly with each other to impact the data used for training the model. As data-centric AI leads to reduced human intervention, the development time is automatically reduced.

How Macgence Can Help?

In the present time, data engineers focus more on improving the quality of data sets being used to train AI models rather than the code it runs on. A model-centric approach was followed in the past which emphasized the coding part. However, it was a less optimized and slow approach. For those looking to build data-centric AI models, reach out to us at Macgence for high-quality datasets.

With Macgence, you get outstanding quality, scalability, expertise, and support. We follow ethical methods to compile data sets that’ll take your AI systems to a whole new height. Macgence is even conformed to ISO-27001, SOC II, GDPR, and HIPAA regulations Ready to elevate your data-centric AI models? Reach out to us today at www.macgence.com!

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