Removing the Shackles on AI Is the Future of Data Science

AI is finally living up to the hype that has surrounded it for decades. While AI is not (yet) the saviour of humanity, it has progressed from concept to reality, and practical applications are improving our environment.

However, much like Clark Kent, many of AI’s astounding exploits are veiled, and its impacts can only be seen when you look past the ordinary mask. Consider BNP Paribas Cardif, a large insurance corporation with operations in more than 30 countries. Every year, the organisation handles around 20 million client calls. They can evaluate the content of calls using speech-to-text technology and natural language processing to satisfy specific business purposes such as controlling sales quality, understanding what customers are saying and what they need, getting a sentiment barometer, and more.”

Consider AES, a leading producer of renewable energy in the United States and around the world. Renewable energy necessitates far more instruments for management and monitoring than traditional energy. AES’ next-level operational effectiveness is driven by data science and AI, which provide data-driven insights that supplement the actions and decisions of performance engineers. This guarantees that uptime requirements are met and that clients receive renewable energy as promptly, efficiently, and cost-effectively as feasible. AES, like Superman, is doing its part to save the planet.

These are only a few of the many AI applications that are already in use. They stand out because, until now, the potential of AI has been constrained by three major constraints:


Compute Power

Traditionally, organizations lacked the computing power required to fuel AI models and keep them operational. Companies have been left wondering if they should rely only on cloud environments for the resources they require, or if they should split their computing investments between cloud and on-premise resources.

On-premises and in-house GPU clusters now provide organisations with options. Several larger, more advanced firms are now investigating production use cases and engaging in their own GPU clusters (i.e., NVIDIA DGX SuperPOD). GPU clusters provide organizations with the dedicated horsepower required to execute enormous training models—as long as they use a software-based distributed computing architecture. A framework of this type can abstract away the problems of manually parsing training workloads across multiple GPU nodes.


Centralized Data

Data has traditionally been collected, processed, and stored in a centralised location, sometimes referred to as a data warehouse, in order to create a single source of truth for businesses to work from.

Maintaining a single data store simplifies regulation, monitoring, and iteration. Companies now have the option of investing in on-premises or cloud computation capability, and there has been a recent push to provide flexibility in data warehousing by decentralizing data.

Data localization regulations can make aggregating data from a spread organization unfeasible. And a fast-growing array of edge use cases for data models is undermining the concept of unique data warehouses.


Training Data

A lack of good data has been a major impediment to the spread of AI. While we are theoretically surrounded by data, gathering and keeping it may be time-consuming, laborious, and costly. There is also the matter of bias. When designing and deploying AI models, they must be balanced and free of bias to ensure that they generate valuable insights while causing no harm. However, data, like the real world, has bias. And if you want to scale your usage of models, you’ll need a lot of data.

To address these issues, businesses are turning to synthetic data. In fact, synthetic data is skyrocketing. According to Gartner, by 2024, 60% of data for AI applications would be synthetic. The nature of the data (actual or synthetic) is unimportant to data scientists. What matters is the data’s quality. Synthetic data eliminates the possibility of prejudice. It’s also simple to scale and less expensive to obtain. Businesses can also receive pre-tagged data with synthetic data, which drastically reduces the amount of time and resources required to build and generate the feedstock to develop your models.


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