A recent report by Statista predicted that the global healthcare AI market would be worth almost $188 billion by 2030.
"The healthcare industry is using machine learning and other emerging technologies to predict diseases, develop medications, conduct AI-assisted surgeries and medical imaging, and many more," shares Phani Teja Nallamothu, machine learning operations (MLOps) senior engineer.
However, mission-critical capabilities are required for a successful at-scale operationalising of AI in healthcare.
According to Teja, scaling AI requires a specific approach that must include three areas of expertise: machine learning, data engineering, and development operations. All three disciplines need to work efficiently to successfully implement machine learning models.
1. Machine learning
Data scientists employ machine learning when they connect to a central data source supplied by data engineers and do model training, validation, hyper-parameter tuning, and re-training. Scalable machine learning is driven mainly by deep learning algorithms, which take complex data and simplify it to identify anomalies.
2. Data engineering
This area of expertise connects various data sources across organisations to collect, clean, transform, and store them in a centralised location. Data scientists then utilise this information to train machine learning algorithms. Efficiency in data gathering and cleansing is necessary. If there is a delay, data scientists will need more information to train their models.
3. Development operations (DevOps)
In DevOps, engineers perform all the tasks required to bring trained machine learning models into operation. This area involves provisioning infrastructure, creating CI/CD pipelines for deployments, and configuring observability tooling for the models in production. Strong DevOps is essential to deploy hundreds of machine learning models efficiently and, in effect, maintain the business value generated by data science models.
To address issues in the efficiency and effectiveness of AI systems, organisations turn to MLOps engineers like Teja, who are experts in the three areas.
"Healthcare companies and other large organisations with complex technological requirements are turning to MLOps engineers who can do the job of a data engineer, a data scientist, and a DevOps engineer," Teja expounds.
While AI adoption in healthcare remains challenging, hiring an MLOps engineer can positively impact and advance digitalisation goals.
Teja is an expert at developing MLOps platforms for large organisations in healthcare and other major industries. He has a wide skill set across machine learning, data engineering, and development and operations, which enable him to build deployment platforms. These include various applications, scalable database solutions, observability tooling, CI/CD pipelines, infrastructure automation, and other development platforms for software engineers and data scientists.
The potential of MLOps in healthcare is exciting and far-reaching. This niche field can help healthcare organisations automate manual processes, reduce costs, and improve the quality of care. It can also be used to identify areas of improvement within healthcare organisations and enable them to better manage their data and resources.
In the future, healthcare professionals could use MLOps to automate decision-making, reduce administrative costs, enable predictive analytics, and provide personalised healthcare services. Teja's expertise in MLOps will play an even more prominent role in the future in managing, securing, and optimising the data and machine learning models that fuel AI-driven initiatives.