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Machine Learning

▾ 1 MINUTE READ — CLOSE

Machine learning moves beyond the traditional model of computation. Instead of arriving at a definite reproducible answer through a series of calculations, machine learning — a branch of artificial intelligence — works instead on a series of statistical probabilities to suggest new solutions to a problem. This work is useful for such jobs as designing new materials, medical diagnosis, advanced game graphics, and so many other tasks.

Much of the early success in machine learning has come from supervised learning, where a clearly defined data set is already available for analysis. But work has been going on to move beyond this model, with the Reinforcement Learning, where an agent learns by interacting with its environment. Gathering even more momentum has been Deep Learning, which doesn’t require all the intermediate steps that supervised learning does. Instead, the idea is to let the Deep Learning neural nets find the answers on their own.

At The New Stack, we have focused our coverage of this emerging field mostly around two areas of scalable architecture. We are keeping a close eye on an emerging field of AIOps, where machine learning can influence and drive IT operations. AIOps should be able to help by automating the path from development to production, predicting the effect of deployment on production and automatically responding to changes in how the production environment is performing. Companies such as New Relic, OpsRamp, and Moogsoft have all invested heavily in this area,

Another area of machine learning we are covering closely is how Kubernetes and related cloud native technologies can expedite the machine learning lifecycle.  Machine learning involves an entire IT cycle of technologies that are very early on in terms of productization: Data must be harvested and cleansed, models must be tested and the most useful models must be pressed into production, with a feedback loop of some sort to ensure the models can be updated. Emerging workflows such as Kubeflow and Anaconda can help streamline these processes.


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Machine Learning

This Robot Is Learning from Humans through Virtual Reality

21 Dec 2017 11:00am, by Kimberley Mok

Data / Machine Learning

The PredictionIO Machine Learning Project Propels Intelligent Development

20 Dec 2017 3:00am, by Susan Hall

Cloud Native / Data / Development / Machine Learning / Open Source

Trilio Data is a Time Machine for OpenStack Data Backup and Recovery

18 Dec 2017 4:27pm, by Swapnil Bhartiya

Machine Learning

Mind-Reading AI Can Reconstruct Videos Using Your Brain Waves

14 Dec 2017 11:00am, by Kimberley Mok

Cloud Native / Machine Learning

Red Hat’s Radhesh Balakrishnan on the Evolution of OpenStack

13 Dec 2017 2:00pm, by Swapnil Bhartiya

Containers / Machine Learning / Monitoring

jClarity Brings Accurate Java Monitoring into the Container World

13 Dec 2017 12:00pm, by Susan Hall

Development / Kubernetes / Machine Learning

This Week in Programming: The Bright Future of Not Dealing with Servers

9 Dec 2017 6:00am, by Mike Melanson

Culture / Development / Machine Learning

Mathwashing: How Algorithms Can Hide Gender and Racial Biases

8 Dec 2017 9:00am, by Kimberley Mok

Machine Learning

Robot Passes a Medical Licensing Exam for the First Time Ever

7 Dec 2017 5:00pm, by Kimberley Mok

Edge / IoT / Machine Learning

A Deep Dive on AWS DeepLens

6 Dec 2017 12:13pm, by Janakiram MSV

Cloud Native / Culture / Development / Machine Learning / Open Source

IAG Finds Open Source to Be the Best Insurance for the Future

6 Dec 2017 9:27am, by Swapnil Bhartiya

Cloud Services / Containers / Development / Kubernetes / Machine Learning

Amazon SageMaker Automates the Artificial Intelligence Development Pipeline

6 Dec 2017 8:45am, by Joab Jackson

Culture / Machine Learning

Could an AI Generate the First Line of a Novel?

3 Dec 2017 6:00am, by David Cassel

API Management / Data / Development / Machine Learning / Storage

This Week in Programming: Amazon’s Yearly Reinvention

2 Dec 2017 6:00am, by Mike Melanson

Machine Learning

Off-The-Shelf Hacker: Three Approaches to Machine Vision

30 Nov 2017 1:00pm, by drtorq

Machine Learning

How Human Workers Can Control Robots from Their Homes — with Virtual Reality

30 Nov 2017 4:00am, by Kimberley Mok

Cloud Services / Culture / Data / Edge / IoT / Machine Learning

Joseph Sirosh of Microsoft: How AI Can Help the Blind See

27 Nov 2017 1:33pm, by Swapnil Bhartiya

Machine Learning

Finnish Startup Valohai Wants to be the ‘GitHub of Machine Learning’

27 Nov 2017 9:14am, by Susan Hall

Data / Machine Learning / Monitoring / Contributed

Deep Learning Dissected: Use The Force to Simplify Data Training

27 Nov 2017 8:31am, by Adel El-Hallak

Culture / Machine Learning

Meet Sophia, the First Robot to Be Granted Citizenship by a Nation

23 Nov 2017 9:00am, by Kimberley Mok

Development / Machine Learning

This Week in Programming: Real-Time Collaborative Coding Comes to Atom and Visual Studio

18 Nov 2017 6:00am, by Mike Melanson

CI/CD / Development / Machine Learning / Security

GitHub Applies Machine Learning to Alert Your Project Dependencies

17 Nov 2017 3:00am, by Michelle Gienow

Cloud Native / Data / Development / Machine Learning

Finding the Right Fit with Data-Driven Applications and Cloud Foundry

13 Nov 2017 2:00pm, by Kiran Oliver and Alex Williams

Machine Learning / Security / Storage

AI Startup Cracks CAPTCHA Codes with Human-Like Vision

13 Nov 2017 6:00am, by Kimberley Mok

Culture / Machine Learning

Can YouTube’s Algorithms Identify Safe-for-Children Videos?

12 Nov 2017 6:00am, by David Cassel

CI/CD / Containers / Data / Development / Machine Learning / Storage

This Week in Programming: Kotlin Eats Your Lunch

11 Nov 2017 6:00am, by Mike Melanson

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