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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

Accident Report: The Google Self-Driving Car

12 Mar 2016 7:35am, by Joab Jackson

Cloud Native / Data / Development / DevOps / Machine Learning

Etsy CTO Q&A: We Need Software Engineers, Not Developers

3 Mar 2016 7:29am, by Joab Jackson

API Management / Development / Machine Learning

How APIs and Artificial Intelligence Help Democratize Election Information

1 Mar 2016 4:43am, by Jennifer Riggins

Culture / Machine Learning

Machine Learning Deciphers the Howling ‘Dialects’ of Endangered Wolves

14 Feb 2016 6:00am, by Kimberley Mok

Cloud Native / Machine Learning

Artificial ‘Imagination’ Helped Google AI Master Go, the Most Complex Game Ever Invented

31 Jan 2016 7:16am, by Kimberley Mok

Culture / Machine Learning

Remembering Marvin Minsky, the Father of Artificial Intelligence

31 Jan 2016 4:55am, by TC Currie

Culture / Machine Learning

Machines ‘Learn to Learn’ with New Algorithm

3 Jan 2016 4:10pm, by Kimberley Mok

Culture / Development / Machine Learning / Open Source

The Year Behind: Looking Back at 2015 with Remembrance of Debian Creator Ian Murdock

1 Jan 2016 9:00am, by David Cassel

Culture / Machine Learning

The Year Ahead: What’s in Store for Machine Learning?

31 Dec 2015 9:45am, by Kimberley Mok

Development / Machine Learning / Monitoring / Open Source

A Look Inside TensorFlow, Google’s Open Source Deep Learning Framework

1 Dec 2015 12:02pm, by Kimberley Mok

Machine Learning

Apache Incubates IBM SystemML for Scalable Machine Learning

25 Nov 2015 8:32am, by Susan Hall

Cloud Native / Machine Learning / Monitoring / Sponsored

Ruxit’s AI Engine in a Dynatrace Culture Used to Living by the Rules

16 Oct 2015 7:35am, by Scott M. Fulton III

API Management / Cloud Native / Machine Learning / Serverless / Sponsored

The Metaphors, the Continua and Jedi Mind Tricks

13 Oct 2015 10:33am, by Luke Lefler

Cloud Native / Cloud Services / Machine Learning

How IBM Would Plant its Bluemix Cloud Behind Your Firewall

3 Oct 2015 9:32am, by Joab Jackson

Machine Learning / Microservices

Pneuron Offers What it Calls “Microservices in a Box”

25 Sep 2015 9:59am, by Scott M. Fulton III

Culture / Machine Learning

Bizarre Ultra-Efficient Hotel Run by Robots Opens in Japan

9 Aug 2015 6:15am, by Kimberley Mok

Culture / Machine Learning

The Rise of Emotionally Intelligent Machines That Know How You Feel

7 Aug 2015 6:55am, by Kimberley Mok

Culture / Machine Learning

This is What Happens When Deep Learning Neural Networks Hallucinate

2 Aug 2015 8:00am, by Kimberley Mok

Machine Learning

IBM: If the Customer Thinks It’s AI, Then It Is

16 Jun 2015 2:49pm, by Scott M. Fulton III

Machine Learning / Microservices

Robots are Back, or, ‘Oh, No, Not Another One’

3 Jun 2015 9:58am, by Scott M. Fulton III

Machine Learning

Google I/O Redux: The AI Engine and the Android Push

2 Jun 2015 7:07am, by Luke Lefler

Culture / Machine Learning

Hunting Down Asteroids with Machine Learning and a World of Programmers

11 Apr 2015 7:00am, by David Bolton

Data / Machine Learning

What Machine Learning Can and Can’t Do

26 Mar 2015 5:00am, by Mark Boyd

Machine Learning

How Argyle Data Uses Facebook’s PrestoDB and Apache Accumulo to Detect Fraud

21 Mar 2015 8:30am, by Scott M. Fulton III

Machine Learning

How Facebook’s Open AI Research Uses GPU Neural Networks

31 Jan 2015 11:00am, by Simon Bisson

Machine Learning

Building Adaptive Apps Like Google Now with APIs and Analytics with Apigee Insights

10 Sep 2014 9:21am, by Alex Williams

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