Machine Learning in Healthcare: Improving Healthcare with MLOps

Machine learning has had a positive impact on almost every industry in the world. Healthcare is no exception, and there are many ways where machine learning is helping healthcare. For example, we are now building smart applications that can turn data into predictions about what diseases certain patients will have in the future.

MLOps is one facet of machine learning that is improving the way we work with healthcare. It allows us to create a digitized healthcare system that is faster and more efficient. Machine learning operations is essentially applying DevOps principles to the machine learning industry. It makes implementing ML in healthcare a breeze.

What Is MLOps?

As mentioned above, machine learning operations is essentially applying DevOps principles the machine learning projects. DevOps focuses on continuous integration, testing, and deployment. MLOps uses those similar concepts, but he focuses on the pieces that make a machine learning project work. For example, data and models are a big part of machine learning projects. These do not exist when creating software projects.

The main differences have to do with the type of data you work with to make the project work. In machine learning, you start with the model building phase before you even get to the implementation that is done by machine learning engineers. That makes it crucial for you to have a good understanding of what it takes to turn data into a model into a working program.

Machine Learning In Healthcare

The main contribution of machine learning to healthcare is in creating a digitized healthcare system that is easy to turn into actionable insights. There are many applied health analytics processes that are coming to play as well. For example, we can turn patient data into predictions that are quite accurate. In fact, there are a few machine learning algorithms that can do this already.

Healthcare providers are even using data to build predictive models for various diseases. It will help with the diagnostic process, which can streamline patient care. As hospitals and healthcare companies gather more data, they will be able to create more accurate and responsive algorithms to help patients get the healthcare outcomes they desire.

On top of these, Machine Learning and Artificial Intelligence are also used in healthcare for:

• Intelligent chatbots
• Advanced Robotics for Operations
• Analytics to detect diseases more quickly
• Smarter hospital management systems
• Faster drug discovery

How MLOps Streamlines the Process

Machine learning has been instrumental in changing a lot of things in the industry. Digitized healthcare is now a reality because of machine learning, and we have even seen automation technologies like chatbots as we bring more machine learning in healthcare.

MLOps takes this a step further and makes the machine learning model development process more streamlined. That makes the process of developing machine learning models faster for healthcare providers, which also means they can develop more accurate models at the same time.

Bring Automation to Healthcare

Another area where machine learning is bringing many benefits to the healthcare industry is automation. For example, machine learning is enabling a new level of surgical robotics.

It is helping with diagnostics, which is freeing up workers to do things that are more creative in nature.

It is also enabling a new level of applied healthcare analytics that makes it possible to leverage patient data to do better in the workplace.