Technological advancements continue to improve healthcare systems around the world. However, inefficient systems continue to exist in the medical industry. This problem adds to the rising healthcare costs, an issue that interoperable technologies try to solve.

In 2018, the World Economic Forum reported that the Organization for Economic Co-operation and Development (OECD) estimated a wastage of 20% of worldwide healthcare expenditures. The report identified system inefficiencies, overtreatment, and improper care delivery as underlying causes of the wasted spending.

How AI and MLOps can improve healthcare

According to Senior AI/MLOps Engineer Phani Teja Nallamothu, using AI-driven technologies has great potential for resolving inefficiencies in the healthcare industry.

“Machine Learning is being used in the healthcare industry to predict diseases, develop medications, AI-assisted surgeries, medical imaging, and many other things,” Phani Teja adds.

AI-based mental health apps now help deliver mental health support more efficiently. The capability to detect diseases is also faster and more accurate with the help of AI. For example, research indicates that deep learning algorithms can detect Alzheimer’s disease at an early stage. Using artificial intelligence, researchers can see retinal photographs in a broader context and identify more specific disease characteristics.

“MLOps platforms allow data scientists to rapidly deploy machine learning models into production while focusing on building machine learning models,” Phani Teja expounds.

Saving millions through AI/MLOps deployment

Deploying AI/MLOps can be extremely valuable. According to Phani Teja, building MLOps platforms in-house can help companies save millions of dollars.

“Each year, organizations pay millions in licensing fees to use MLOps capabilities provided by third-party vendors. By developing these platforms internally, businesses can tailor them to their specific business requirements, which is not always possible with third-party solutions,” he shares.

Besides saving on licensing fees, MLOPs allow businesses to reduce labor costs. Phani Teja shares, “Organizations would not need to hire three individuals with three distinct skill sets; they can hire a single MLOps engineer to complete the task.”

As an expert in machine learning, data engineering, and DevOps, Phani Teja has developed mission-critical platforms for large enterprises. The scalable MLOps platforms he developed enabled those businesses to solve major data problems and derive previously inaccessible business value.

Accessing open-source tools

An end-to-end, open-source MLOps data science platform can benefit large organizations by reducing operational costs, increasing operational efficiency, and eliminating vendor lock-in. Open-source solutions are typically more flexible and can be tailored to an organization’s specific needs. It can also be more cost-effective than proprietary solutions, making them ideal for large organizations looking to save on expenses.

“Open-source MLOPs will save companies millions of dollars annually, which in turn will reduce the cost of these services for consumers,” Phani Teja shares.

These solutions are also more secure, as they are open to public critique and continuous maintenance and improvement from a large community of developers.