In June, Microsoft available new Azure Machine Learning (ML) courses on Udacity, providing interested parties with open source tools code and frameworks like PyTorch for building 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 he uses Azure ML and PyTorch to accelerate research into new drug development.
The team believes that the machine learning είναι το κλειδί στην ανάλυση δεδομένων για την εύρεση σχετικών συνδέσεων, γι ‘αυτό χρησιμοποίησε μια προσέγγιση βασισμένη σε γραφήματα γνώσης για να κατανοήσει τη σχέση μεταξύ των δικτύων των επιστημονικών δεδομένων με βάση τα συμφραζόμενα.
The members of the natural language processing (NLP from natural language processing) preferred the PyTorch framework for the construction of 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.
Likewise, end-to-end lifecycle management for the entire procedure of machine learning is also facilitated through Azure ML, as shown in the figure above, speeding up iterations and the model development process.
The models constructed using these approaches are ultimately used to discover and propose “potential new and novel drug targets” in a faster and more accurate manner.
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.
