Justin Gage

Justin Gage
Growth and Data
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Recent Posts

August 06, 2018

Introduction to Dataset Augmentation and Expansion

Source: TDS

If your neural nets are getting larger and larger but your training sets aren’t, you’re going to hit an accuracy wall. If you want to train better models with less data, I’ve got good news for you.

Dataset augmentation – the process of applying simple and complex transformations like...

August 02, 2018

Vertical Spotlight: Machine Learning for Healthcare Diagnostics

Source: Case Engineering

Diagnostics is part of the core of healthcare — research suggests a third of all Healthcare AI SaaS companies are tackling just this sector.

Machine Learning can automate parts of the diagnostic stack, aid doctors in deciding how to interpret tests, and greatly reduce...

July 24, 2018

Vertical Spotlight: Machine Learning for financial fraud

For every dollar of fraud that financial services companies suffer, they incur $2.67 in costs to their business. With more entry points in the digital age and increasingly sophisticated attackers, tackling fraud manually is quickly fading to irrelevance: but Machine Learning offers a promising way...

July 02, 2018

How to integrate Machine Learning into your Slack channels

Slack is one of the fastest growing companies of all time, and there’s a good chance it’s also the messaging app that you use for work. The Algorithmia Slack Client lets you integrate Machine Learning into your Slack channels – both as slack commands and as bot users – giving you more firepower on...

June 25, 2018

How to version control your production Machine Learning models

Source: KDnuggets

Machine Learning is about rapid experimentation and iteration, and without keeping track of your modeling history you won’t be able to learn much. Versioning let’s you keep track of all of your models, how well they’ve done, and what hyperparameters you used to get there. This...

June 21, 2018

Machine Learning and Mobile: Deploying Models on The Edge

Source: TensorFlow

Machine Learning is emerging as a serious technology just as mobile is becoming the default method of consumption, and that’s leading to some interesting possibilities. Smartphones are packing more power by the year, and some are even overtaking desktop computers in speed and...

June 11, 2018

Why a multi-cloud infrastructure is an important part of application and Machine Learning deployment

Source: Forgeahead

Multi-cloud is quickly becoming the de facto strategy for large companies looking to diversify their IT efforts. At Algorithmia, we deploy across multiple clouds and recommend it for Machine Learning pipelines and portfolios. This post will outline the pros and cons of a...

June 06, 2018

Data Scientists and Deploying Machine Learning into Production: Not a Great Match

Source: Timo Elliott

Asking your Data Scientists to deploy their Machine Learning models at scale is like having your graphic designers decide which sorting algorithm to use; it’s not a good skill fit. The fact of the matter is that in 2018, the standard Data Science curriculum doesn’t prepare...

May 29, 2018

Deploying Machine Learning at Scale

Source: turnoff.us

Deploying Machine Learning models at scale is one of the most pressing challenges faced by the data science community today, and as models get more complex it’s only getting harder. The sad reality: the most common way Machine Learning gets deployed today is powerpoint slides.

May 29, 2018

Deploying Machine Learning at Scale

Source: turnoff.us

Deploying Machine Learning models at scale is one of the most pressing challenges faced by the data science community today, and as models get more complex it’s only getting harder. The sad reality: the most common way Machine Learning gets deployed today is powerpoint slides.

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