A startup called Brainome today launched a new product designed to help data scientists determine how much data they need to sufficiently train their machine learning models. In addition to cutting costs, the software can also help data scientists avoid overfitting their models.
Brainome's new product, Daimensions, seeks to eliminate brute-force aspects of machine learning model development, integrate with existing CI/CD infrastructure and generate small ML models that can run efficiently on commodity hardware.
Brainome takes a fundamentally different approach: measuring information content in data against target types of models before building anything. Brainome’s approach steers teams towards better outcomes, making it possible to predict project speed, costs, and ultimately success.
Brainome featured in webinars
Follow along Brainome's guest appearances in machine learning webinars
In this webinar, we will discuss an entirely new and different approach to supervised machine learning - one that is rooted in measurements. We will explain how this new approach (which is actually as old as science itself) can be used to solve difficult bioinformatics problems, many of which have previously been out of reach. We will explain the fundamentals of how measurements-based machine learning works, and also explore how the approach can be applied to solve real-world problems in bioinformatics & biostatistics.
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The trend in Machine learning for the last 20 years has been that the more data you have and the more you spend on computation the more likely you are to succeed. Is “more data” and “more compute” a thing of the past?
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