In June, Microsoft available new Azure Machine Learning (ML) courses at Udacity, providing open source tools and frameworks such as PyTorch to build complex ML solutions. Last week at Ignite, Microsoft Power BI CVP Arun Ulag mentioned the importance of Azure ML services in building end-to-end systems.
Today, biopharmaceutical company AstraZeneca released information on how it uses Azure ML and PyTorch to accelerate research into new drug development.
The team believes that machine learning is the key to analyzing data to find relevant connections, so it used a knowledge-based graph approach to understand the relationship between contextual scientific data networks.
The members of exwork of natural language (NLP from natural language processing) preferred the PyTorch framework for building various models, following the latest research.
In conjunction with Azure ML, they created proposition systems that can be used to map nodes in the knowledge graph to numerical data. These systems are then used to train the use of specific case models in a coherent manner, through the advanced computing capabilities of the Azure ML.
A recent document AstraZeneca compares the performance of the model under different conditions. The company uses Azure Blob storage to handle the vast amounts of data required.
Similarly, end-to-end lifecycle management for the entire machine learning process is also facilitated through the Azure ML, as shown in the figure above, accelerating iterations and the model development process.
Models built using these approaches are ultimately used to discover and suggest "potential new and new drug targets" in a faster and more accurate way.
In the future, the company plans to continue to expand its knowledge chart - applying machine learning through the aforementioned platforms, with the ultimate goal of building new drugs in the healthcare industry more efficiently.
