Ciro Greco has built ML systems at many named-brand retailers. In this episode, he gives us tips on getting value out of ML at “reasonable scale” companies with NLP and information retrieval. The concept of “reasonable scale” was one he returned to. and he clearly has a nuanced understanding of how that segment differs from the hyper scale tech giants. He also brings advanced ideas like embeddings from NLP towards e-commerce personalization.
Co-Founder and Head of Product
Building Things with Machine Learning is Masterful AI's podcast covering interesting applications of machine learning. We started this podcast to focus less on innovative research, and more on the innovative ways ML is being applied to building products.
Building Things with Machine Learning is Masterful AI's podcast covering interesting applications of machine learning. We started this podcast to focus less on innovative research... and more on innovative ways ML is being applied to building products.
We've released Masterful 0.5.2, the platform that automates model development for computer vision. This release adds support for object detection, allowing developers to build detection models faster than ever before.
You can download it right now with a `pip install -U masterful`.
Previously, we showed that throwing more training data at a deep learning model has rapidly diminishing returns. If doubling your labeling budget won’t move the needle, what next? Consider semi-supervised learning (SSL) to unlock the information in unlabeled data.
It’s hard to stay current and maintain competency in deep learning. It’s a young and fast growing field, which means that groundbreaking research and innovations are coming out really rapidly. But at Masterful, we don’t have a choice: we have to stay current because the promise we make to developers is that our platform automatically delivers state-of-the-art approaches for computer vision models (CV) in a robust and scalable way.
Our mission at Masterful AI is to bring the power and efficiency of modern software development to machine learning. One of the most archaic and error-prone aspects of ML development is getting accurately labeled training data. Through our work with many other ML engineers, we've seen a common fear: no one really knows if simply throwing more labeled training data at their model is going to deliver the accuracy they need. This has big implications, since labeling is slow and expensive. In this post, we'll share a framework and online calculator you can use to evaluate the ROI of spending more money on labeling.