Introduction

If you have used TensorFlow before, you know how easy it is to create a simple neural network model using the Keras API. Just create an instance of the Sequential model class, add the number of desired layers and accompanying layer nodes, define the activation functions to be used by each layer, and compile your model by providing an optimizer and loss function.  Right? While this process is simple enough to grasp conceptually, it can quickly become an ambiguous task for those just getting started in deep learning.

Mar 22, 2023|Life Sciences, Technology

Digital transformation is reshaping industries, demanding scientific organizations to adapt and remain competitive in the rapidly changing landscape. Data lies at the heart of this transformation, providing the foundation for strategic decision-making and innovative breakthroughs. To achieve sustained success and outpace the competition, visionary leaders understand the need to not only keep pace with digital advancements but to stay ahead through continuous innovation and adaptation.

As a company that delivers Digital Transformation for Science, part of our job at Enthought is to understand the trends that will affect how our clients do their science. Below are three trends that caught our attention in 2022 that we predict will take center stage in 2023.

Labs are resetting the trajectory for drug development: reducing timelines from years to months; decreasing costs from billions to millions; and gaining an advantage by delivering drugs to market in months rather than decades.

This value combination is a compelling case for investment in digital capability and organizational transformation.

Scaling innovations in R&D can be considered challenging in two dimensions: from lab to lab; and from the lab to the end customer. A 100x or 1000x advance in R&D efficiency challenges an organization to adapt, from R&D to engineering, manufacturing, and to the marketplace, where there is a significant business opportunity.