Get smarter at making your corporation effective by improving the accuracy of AI systems. Data-centric approaches elevate machine learning models.

Key Takeaways

• Understanding the concept of the Data-Centric AI approach.
• Data correcting techniques across all the AI projects.
• Growth and opportunities in the corporate world.

What is Data-Centric AI?

In simple words, the data-centric approach refers to how data is changed or altered to improve performance and evaluation. Data is the primary encoding of domain knowledge, and it is constantly gaining renewed prominence within Artificial Intelligence (AI) to robust traditional systems.

, a technologist and entrepreneur, is the pioneer of Data-Centric AI. In his keynote on Deep Learning, he discussed the evolution of technology (from ) and presented some valuable insights about its implementation to data individuals, stating that the data-centric approach gives more time in labeling, managing, slicing, and augmenting the data. In another video session, said:

"Data is food for AI"

He further adds:

"Instead of focusing on the code, companies should focus on developing systematic engineering practices for improving data in ways that are reliable, efficient, and systematic. In other words, companies need to move from a model-centric approach to the data-centric approach".

Techniques of Data-Centric AI Competition

The two main techniques used in the are:

Data Labelling

The identification of raw data in images, text, and videos, in the machine learning process refers to data labeling. As emphasized by Andrew Ng, the issue in making a high-quality dataset ascends many times from errors and inconsistencies during data labeling.

However, consistent rules in labeling and strong consensus among professionals enable the stake of fallacies and decrease subjectivity. The most significant aspect of labeling is the consistency to double-check the accuracy of labelers.

Data Balancing

Data balancing has to be in a proper array. For the maintenance of data efficiencies, decision-making is crucial for data teams to enforce consistency.

With the solution, AI and decision-making integrate both Data Science and Machine Learning. Andrew Ng draws attention to the data and modifies iteratively by holding it the same way. More precisely, it detaches high-quality data. To accomplish this effectively, one needs insightful understanding to balance data.

Importance of Data-Centric AI in Business Sectors

According to Andrew Ng:

"The data-centric approach allows people in manufacturing, hospitals, farms to customize the data, making it more feasible for someone without technical training in AI to feed it into an open-source model."

It will provide a surge of further opportunities for AI to make distinctions in traditional ways with minimal data sets and less expertise.

"What I see across the world is lots of these 1 to 5 million dollars projects that aren't being worked on", says Ng.

Companies from various industries, including automotive, electronics, and medical equipment manufacturers, are making headways by using AI and deep learning solutions these years.

Perks of Having Data-Centric AI in the Corporate World

"If 80 percent of our work is data preparation, then ensuring data quality is the important work of a machine learning team." Andrew Ng

Improves Decision-Making

The need to build a data-centric organization entails developing tools, skills, and, most crucially, a factual culture that prioritizes data in decision-making. It is all about gathering and analyzing information to make better decisions.

The pursuit of a data-centric approach enhances decision-making, which is incredibly necessary in today's business world. In particular, it becomes easier for the companies to adopt change that may substantially influence the company's trajectory toward growth.

The recent advancements in data and analytics impacted the automotive industry by having various data-driven features linked to mechanical segments. The vehicle data architectures are redefined due to the underlying data models that seek to provide core analytics and Artificial Intelligence.

Consumers expect the vehicle companies to deliver relevant messages across the channels. Along with preferring actual timings that they have chosen earlier, sometimes, brands fail, and consumers move away. To overcome the issue of hyper-target marketing, it is integral to practice a data-driven approach.

Eradicates the Workflow Challenges

Data-centric AI enables manufacturers to use machine learning even when carrying a small data set in the manufacturing sector. This is where the startup of Ng came into the limelight!

Under one monolithic search engine or AI system, consumer software still works smoothly since it requires multiple enterprises. Each hospital, for example, uses its mode of coding electronic health records. But it should be noted that a single AI model cannot handle the overall forms of hospitals.

Fortunately, Landing AI tech makes it possible for manufacturers to devise sophisticated AI models. It can diminish the number of hiring thousands of engineers to labor on it. However, it is the gateway to countless potential opportunities for employment, notably in the manufacturing industry. The world runs on big data!

Ng is looking ahead of time (in the next five to seven years). He sees a massive breakthrough for AI. There will be a magnificent focus on data-centric AI, shifting away from the consumer software industry. He further elaborates his views by saying,

"What we are missing is a more systematic engineering discipline of treating good data that feeds AI systems, and this is the key to democratizing access to AI."

Reduces the Developmental Time of Projects

Manufacturing teams can operate in parallel, which instantly impacts the data used by the AI system with this approach. It lowers developmental duration by eliminating excessive back and forth between groups and looping in human input most needed. With prompt detection of the damaged parts, risks are notably avoided. It is how machine vision technologies work effectively in such conditions.

For example, a series of images from are used to detect defects that commonly occur during steel sheets manufacturing. However, the system can examine images of products on an assembly line. At least 39 distinct defects need to be identified. The development of a computer vision model having hyperparameters reaches the 76.2% accuracy baseline system. Yet the task is to achieve 90% accuracy.

Quality over Quantity

The platform is immensely beneficial in providing high-quality labeled data sets. Implementing suitable algorithms at the right time is incredibly crucial in attaining error-free outcomes. Nowadays, it is possible and necessary since high-quality data is always a demand in the corporate world.

Having a data-centric approach offers a systematic method that improves data, consensus data, and ultimately the cleansing of inconsistent data. Understanding and knowing how to organize data quality according to their domains give businesses a competitive edge.

Wrapping Up: Organizations Need Data-Centric AI, Data Drives Change!

Benefitting from data-centric AI (machine learning) has become a practical approach for all kinds of industries globally. Without any doubt, AI is the core aspect of decision-making in different business sectors to bring out robust results from all AI systems. By adopting AI solutions, a fundamental shift unleashed the utmost potential of Artificial Intelligence (AI) in the current digital age that will prolong for the coming years.

Are you ready to get started with Data-Centric AI? Here are some and an insightful to help you take the next step. Good luck! 👍

References

Felicia Hou,

Gil Press,

, , (2021)