Blog posts

The latest on Masterful and tips on Machine Learning

Posts by

Yaoshiang Ho

Co-Founder and Head of Product

Masterful AI Developer Newsletter - July 2022

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Yaoshiang Ho
CLI

Hope you've been enjoying summer! 

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Announcing Masterful 0.4.1

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Yaoshiang Ho

We’re excited to announce the v0.4.1 release of Masterful, the training platform for computer vision models!

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It's time to use Semi-Supervised Learning for your CV models

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Yaoshiang Ho

Intro

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. 

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Announcing Masterful v0.4

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Yaoshiang Ho

Version 0.4 of Masterful

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Four buckets of deep learning innovation: architecture, data, regularization, and optimization

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Yaoshiang Ho

Intro

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. 

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Is More Data Labeling Worth it?

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Yaoshiang Ho

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.

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