Machine learning for R&D: From hot topic to practical application
JM’s laid out three tools and mindsets for enhancing data liquidity throughout the R&D lifecycle: machine learning, collaboration as a competitive advantage, and modern cloud security.
All of these concepts ladder up to our fundamental belief that connecting your R&D workflows will reduce friction, accelerate timelines, improve scientific outcomes, and, ultimately, unlock the full power of biotech. Here's the recap of our three presentations on the subject and how Benchling might be able to help future-ready your R&D:
Machine learning is one of the hottest topics in science and technology, but what does it look like when you need to apply it to real-world problems?
For most scientists who work in a fragmented ecosystem of point solutions like email, spreadsheets, and antiquated software, the answer is: it doesn’t look good. They face three major problems:
- Siloed Data. There’s too much data, and it’s stuck in silos without any consistency in how it gets captured. That means either the most helpful information is never uncovered and shared, or if it is, the process of sharing is time-consuming and error-prone.
- Limited Collaboration. As teams discover how essential collaboration is to get a product out the door, they also learn that the legacy systems they use are not built to enable real-time, cross-team engagement.
- Bottlenecked Insights. Everyone from bench scientists to R&D leaders requires access to insights that help them make better decisions—from understanding the results of an experiment to mapping overall program performance and pinpointing bottlenecks.
Collaboration as a competitive advantage
We believe that collaboration is the key to making breakthroughs in the lab. Currently, however, collaboration is not the norm. Scientists are still recording too many experiments in siloed systems like spreadsheets. They might compare data across their own experiments, but they often struggle to see the impact they have on other teams or departments.
Modern cloud security: Dispelling common myths about cloud computing
The cloud has been with us for two decades now, and it's time for us to face the fact that many of our ideas about cloud security aren't living up to the realities of today's world.
Over the last two decades, cloud computing has matured, approaches to security have advanced considerably, and data liquidity has become the expectation. But many commonly held beliefs about cloud computing have prevented some industries and organizations from adopting a strategy of digital transformation. Many of these are myths that don't hold up to scrutiny.
The world has changed. With new technologies like machine learning and cloud computing, we are seeing more and more innovation in every industry.
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