AI is no longer a futuristic concept – it’s a mainstay in our daily lives, both personally and professionally. In the business world, AI is revolutionizing workflows, driving efficiency and speeding up processes.
However, as organizations rush to benefit from this modern technology, they must prioritize the ethical and transparent use of AI, producing outputs that can be trusted and explained.
Balancing the adoption of rapidly evolving technology with ethical considerations is no small feat. All the while, companies are under constant pressure to reduce costs while providing tools that are accessible to everyone, from savvy experts to beginners. Leaders are grappling with talent shortages and the challenge of deploying and maintaining AI models effectively. Often, models fall short due to a lack of robust data and AI life cycle – an essential framework for managing data, developing models, and ensuring their successful deployment and monitoring. This life cycle is essential for extracting valuable business insights and making informed decisions.
As organizations navigate this AI-driven landscape, it’s clear that a comprehensive approach to data and AI is vital for sustained success and innovation.
Supporting the data and AI life cycle
To address these challenges, a technology platform must support critical areas of the data and AI life cycle: data management, model development, and model deployment and maintenance. Distinct roles – such as data engineers, data scientists, and MLOps engineers, are responsible for different steps within this life cycle, and the platform must be flexible enough to meet their varying needs.
Addressing ethical concerns in AI requires a comprehensive strategy focused on fairness, transparency and accountability. Without a clear understanding of how AI algorithms reach conclusions, there is a risk of perpetuating societal inequalities and eroding trust in their decisions. Vrushali Sawant, Data Scientist, SAS
For example, data engineers focus on data management, which is crucial not only for generating trustworthy model outputs but also for enhancing productivity. Issues like disparate data, inconsistencies and incompatible data types can slow down model development and expose organizations to privacy and governance risks. Therefore, robust data management must be integrated into the platform.
Accommodating skill levels and handoffs
A data and AI platform must also cater to different skill levels and facilitate efficient project handoffs to mitigate risks and improve model development and deployment. It should have an infrastructure capable of supporting scalable AI workloads – both up and down – with flexibility to control costs.
Market requirements and the need for collaboration
The market continues to evolve to support various roles working with data and AI. Both commercial and non-commercial approaches, like open-source solutions, offer flexibility and lower costs. Commercial solutions provide flexibility for both non-technical and technical users and support the entire data and AI life cycle with cloud-based workloads.
Greater collaboration is achieved when data and AI platforms support diverse roles, such as data engineers, data scientists, MLOps engineers, and business analysts. Working within a single platform enables teams to complete the end-to-end data and AI life cycle effectively.
Ethical use and explainable outputs
With any rapidly evolving technology, there are risks. Model outputs must be traceable and explainable to mitigate bias and risk. Understanding what goes into a model is essential to understanding its outputs. The platform must facilitate collaboration among data engineers, data scientists, and MLOps engineers to ensure key connections are made.
“Addressing ethical concerns in AI requires a comprehensive strategy focused on fairness, transparency and accountability,” said Vrushali Sawant, Data Scientist, Data Ethics Practice at SAS. “Without a clear understanding of how AI algorithms reach conclusions, there is a risk of perpetuating societal inequalities and eroding trust in their decisions.”
Regulated industries need to build, train and test models but face data privacy or restriction challenges. Introducing modern technologies, such as synthetic data, into the data and AI platform can overcome these concerns and accelerate model development and deployment. For instance, in health care, synthetic data can help solve rare diseases by filling data gaps, while in the financial industry, it can address data privacy restrictions.
Adapting to the evolving technology landscape
Data and AI have become intertwined and codependent for success in an AI-driven business world. Overcoming common data and AI challenges will increase productivity and trust in model outputs. Organizations can fully adopt AI responsibly and achieve significant productivity gains while driving down costs, offering tools that suit varying skill levels and working within an end-to-end platform to support the data and AI life cycle.