AI/ML Case Study
Forecasting Improvements with Azure Machine Learning Implementation for a Global Medical Device Company
Client
The client is a global medical technology company specializing in the development of innovative solutions for treating cardiovascular diseases and neuromodulation disorders. Their product portfolio focuses on improving patient outcomes in areas such as cardiac surgery, neuromodulation for epilepsy, and sleep apnea therapy. With operations across more than 100 countries, the company aims to advance healthcare through cutting-edge technology and research.
Project Scope
Current machine learning processes are creating data science challenges
The client has thousands of medical devices currently implanted in patients. These devices will eventually need to be replaced, and new implants put in. The company has a process to identify the future demand for these devices, so they can be ready to meet the needs of their patients. The client looks at past data and tries to forecast future trends for end-of-life replacements on a yearly and a 5-10 year forecast.
Currently, the client uses a machine learning process to accomplish this forecast, however, it is a predated system and not as organized, secured, and governed as it could be. The company was using local machines which would lead to computing times of up to 2 days to run one process, not to mention the data privacy and security concerns. This also hindered them from using the most up to date data and AI techniques.
The main objective for the client is to move their data science workloads to the Azure Machine Learning (Azure ML) platform. Azure ML addresses traditional machine learning challenges by offering automated processes, robust data security, and scalable cloud infrastructure.
Key Challenges
A localized system is leading to a multitude of issues for the data science team
The Smartbridge Solution
Implementing an enterprise-level Azure Machine Learning platform
The Smartbridge team used MLOps (Machine Learning Operations) practices to help the client enable collaboration and streamline their processes. MLOps is a set of practices that combine machine learning, DevOps, and data engineering principles to automate and standardize the development, deployment, and monitoring of machine learning models. By implementing MLOps, the team ensured that the machine learning lifecycle—covering model development, testing, deployment, and ongoing monitoring—was fully integrated into a scalable and efficient process.
The team started by transitioning disparate data science workloads to the Azure ML platform, which not only enabled seamless integration, scalability, and security but also provided access to a wide range of tools and services necessary for efficient model management.
In addition to migrating to Azure ML, the team re-factored the project to align with the ML lifecycle, making the solution monitorable, maintainable, and adaptable to future changes. By building a pipeline and organizing each step as an individual block, the team ensured that the system is highly modular. This modularity offers several benefits:
When using Azure, Microsoft recommends using Python, but as previously stated, the client’s data science team uses R as their programming language. Smartbridge solved this discrepancy by creating a Python wrapper to go around the R code, so the client can still use codes they previously created and feel comfortable continuing using the language they are used to until they can pick up Python.
To resolve the issue of security, the team put the data in a secured storage container and create a secured connection to the Azure Machine Learning environment. This eliminates the need for data to be downloaded to local machines in order to be used allowing the company to stay in full compliance with corporate and healthcare policies.
Finally, Smartbridge was also able to implement versioning for models which allowed the client to retrain models without losing their original versions, so they can do things like compare and validate results.
Overall Solution:
Value Delivered
Creating a 70% reduction in processing time
With the Azure Machine Learning environment, the client can run multiple steps of the pipeline in parallel rather than having to run the entire pipeline for their calculations. This reduced the time it took to complete this process from ~40 hours to ~12 hours and therefore improving their forecasting.
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