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.

May 23, 2018

Exploring the Deep Learning Framework PyTorch

Anyone who is interested in deep learning has likely gotten their hands dirty at some point playing around with Tensorflow, Google's open source deep learning framework. Tensorflow has a lot of benefits like wide-scale adoption, deployment on mobile, and support for distributed computing, but it...

May 16, 2018

Investigating User Experience with Natural Language Analysis

User experience and customer support are integral to every company's success. But it's not easy to understand what users are thinking or how they are feeling, even when you read every single user message that comes in through feedback forms or customer support software. With Natural Language...

May 14, 2018

Introduction to Reinforcement Learning

We only understand a sliver of how the brain works, but we do know that it often learns through trial and error. We’re rewarded when we do good things and punished when we do the wrong ones; that’s how we figure out how to live. Reinforcement Learning puts computational power behind that exact...

May 08, 2018

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