As cloud computing continues attracting the attention of many industries and disciplines, life sciences appear to be taking the right strides towards the use of cloud computing platforms. The greatest benefit of this technology in this discipline is its scalability, innovation, and flexibility. The cloud-based technologies give life sciences the capability to access more resources and processing scale. With the cloud, datasets can be anonymized and shared with ease. This will be a great enabler of the new strategies on artificial intelligence, which include drug discovery. However, some experts have warned that the cloud will not automatically create benefits, reduce costs, or enhance security, that is one of the requirements in any discipline. It is upon each organization or user to determine the processes that can be removed through a transition to the cloud and the new capabilities that can be accessed through the cloud platforms.
Amazon recently released AutoGlun, the open source toolkit for automated machine learning. Developing machine learning applications can be very involved with essential multiple layers as a requirement. For example, developers must define the parameters for choices in construction of the AI model and spend considerable amounts of time with data set pre-processing. They also need to consider finding the best architecture design for their AI models. AWS has a considerable amount of tools to develop AI applications.
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