Frameworks of MLOps in Healthcare
MLOps in healthcare involves monitoring, deploying and managing machine learning algorithms in production. Here are some frameworks used in MLOps in Healthcare that are discussed as:
- TensorFlow Extended (TFX): TensorFlow Extended is a platform that is designed for deploying the machine learning algorithm on the production integrating with TensorFlow that make it easy for healthcare machine that is based on TensorFlow. TFX can be used for medical imaging, patient diagnostics, and other healthcare applications.
- MLFlow: Open-source platform that is used to manage machine learning algorithm on MLOps. It involves packaging code and share and deploy models in production. MLFlow used in healthcare for tracking experiments while developing machine learning algorithms and manage and deploy these machine learning algorithms in production.
- Kubeflow: Open-source platform used for managing, deploying machine learning algorithm and Kubeflow is built on Kubernetes. Kubeflow in healthcare used for monitoring real-time patient or predictive analytics.
- Version Control of health data: Data Version control is an open-source platform where the version of the machine learning algorithms is controlled. DVC helps in managing the version of the data sets. It is helpful in healthcare scenarios where data is important for training and validating machine learning algorithms conforming the changes in data.
Will MLOps Change the Future of the Healthcare System?
In the era of modern technology, innovations are going on day by day. Machine learning and Artificial intelligence make it easy to solve daily problems by automating tasks and reducing the human cost. Machine learning operations are used to automate the deployment, monitor, and manage machine learning models in production. In healthcare systems, many clinical and laboratory activities are done with the help of analyzing a lot of data. Machine learning algorithms and deep learning algorithms analyze this and extract insights from this data that solve the problems related to healthcare. MLOps finds the solution in many sectors such as finance, healthcare in the retail industry.
In this article, we will see How MLOps change the future of the healthcare system, the Importance of MLOps in Healthcare, How Machine learning Reinventing MLOps, its Uses, and the future of MLOps.
Table of Content
- Introduction
- Importance of MLOps in Healthcare
- How is Machine Learning Reinventing Healthcare?
- Use of MLOps in Healthcare
- Real-Life Examples of MLOps in Healthcare: Ortho Baltic Case Study
- Frameworks of MLOps in Healthcare:
- What is the Healthcare DataScientist Do?
- Future of MLOps in Healthcare systems
- Conclusion
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