Platforms for Machine Learning Operations (MLOps) have enabled data scientists to put models in production quickly, assisting major industries in solving big data problems. This digital transformation has also benefited the healthcare systems by predicting diseases, developing medications, and conducting surgeries assisted by Artificial Intelligence (AI).
Phani Teja Nallamothu, Senior Cloud Engineer II at Strava, has built MLOps platforms on on-premise, cloud, and hybrid environments for healthcare-related companies. He shares the latest AI/MLOps technologies' emerging roles in making the industry safer and more efficient.
Bringing healthcare MLOps to the next level
MLOps enables healthcare providers to quickly and easily deploy and manage machine learning models in production. Healthcare organizations can develop and deploy AI applications faster and more securely while automating and optimizing their processes.
With the platform, healthcare providers take advantage of predictive analytics, automation, and other emerging technologies to improve patient care, reduce costs, and streamline operations.
MLOps can also help healthcare organizations collaborate more easily with other stakeholders, such as physicians, specialists, researchers, and insurers, to share and analyze data to optimize outcomes.
"Healthcare organizations can have on-demand storage and computing resources to train their machine learning models with the least waiting time possible," Phani Teja expounds.
An MLOps platform allows machine learning engineers to conduct model training and production deployment models and monitor these activities effectively. The platform encompasses efficient logging, monitoring, and alerting frameworks as part of its core capability.
The looming challenge with healthcare digitalization
Once the models are deployed in production, data scientists need a way to look at how the models are performing and get alerted when something goes wrong. Some companies spend millions of dollars to buy these platforms from third-party companies.
While several healthcare companies have already transitioned to MLOps in their critical operations, Phani Teja explains that trends are fast-changing, requiring a dynamic internal talent to navigate new technologies being introduced.
These tedious steps can be highly reduced now that AI and machine learning have become widely accessible. MLOps platforms can be built in-house using open-source technologies, just like what Phani Teja has accomplished for his employers and clients.
Building MLOps expertise internally
MLOps is a niche field needing expertise from three areas, which are generally costly and require extensive specializations: Machine Learning, DevOps, and Data Engineering. Machine learning model training alone is computationally expensive and time-consuming, and data scientists need on-demand storage and computing resources to train their machine learning models.
"Each field has evolved a lot in the last decade. You have to constantly keep up with the latest trends in each field and use them to build MLOps platforms used by different healthcare-related organizations," Phani Teja shares. His expertise in all three fields allowed him to continue building technology platforms to improve the health and wellness of people.
Phani Teja aims to bring an end-to-end MLOps data science platform and open source so every organization, small, medium, or large, can use it for free. Achieving this goal could help reduce millions of people's health and medical services costs.