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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

AI Researchers Create Self-Replicating Neural Network

19 Apr 2018 11:00am, by Kimberley Mok

Machine Learning

Veritone Offers AI-as-a-Service for Developers

17 Apr 2018 6:00am, by TC Currie

Machine Learning / Sponsored / Contributed

Easily Move TensorFlow from Dev to Prod with DC/OS, Jupyter, and BeakerX

13 Apr 2018 9:00am, by Chris Gutierrez

Machine Learning

Anaconda’s Python/R Distribution Sets the Stage for Scalable Machine Learning

11 Apr 2018 9:15am, by Joab Jackson

Machine Learning

Swim: A Raw Compute Platform that Poses Questions About the Endless Fabrics of Time

5 Apr 2018 12:49pm, by Alex Williams

Machine Learning

OpenAI Algorithm Allows AI to Learn from Its Mistakes

5 Apr 2018 11:00am, by Kimberley Mok

Machine Learning

How Good Is Machine Learning at Understanding Text?

3 Apr 2018 3:00am, by Mary Branscombe

Containers / Machine Learning

This Week on The New Stack: Hykes Leaves Docker, Nvidia Joins the Kubernetes Movement

30 Mar 2018 2:31pm, by Libby Clark

Machine Learning

Nvidia Embraces Kubernetes for Scalable Deep Learning

28 Mar 2018 1:27pm, by Joab Jackson

Machine Learning

Nvidia Offers a Data Center Simulator for Training Self-Driving Automobiles

27 Mar 2018 11:07am, by Joab Jackson

Development / Machine Learning

This Week in Programming: Big Blue’s AI Play for the Enterprise

24 Mar 2018 6:00am, by Mike Melanson

Machine Learning / Contributed

Building Blocks for AI: Mutual Information and the Pearson Correlation

23 Mar 2018 8:58am, by Ebrahim Safavi

Machine Learning

Azure CTO: Open Source Is Key to Machine Learning in the Cloud, or on the Edge

22 Mar 2018 2:41pm, by Mary Branscombe

Machine Learning

AI Algorithm with ‘Social Skills’ Cooperates Better Than Humans

22 Mar 2018 11:00am, by Kimberley Mok

Machine Learning / Contributed

How Deep Learning Supercharges Natural Language Processing

20 Mar 2018 3:00am, by Jacob Perkins

Machine Learning / Security / Sponsored / Contributed

Machine Learning and Beyond: Algorithmic Detection in Security

19 Mar 2018 8:37am, by Vishwanath Raman

Culture / Machine Learning

Off-The-Shelf Hacker: Could Your Project Use Some Artificial Intelligence?

14 Mar 2018 6:00am, by drtorq

CI/CD / Cloud Native / Data / Kubernetes / Machine Learning

Spark 2.3 Brings ‘Continuous Processing’ to the Big Data World

14 Mar 2018 3:00am, by Scott M. Fulton III

Containers / Kubernetes / Machine Learning

A Primer on Nvidia-Docker — Where Containers Meet GPUs

9 Mar 2018 8:33am, by Janakiram MSV

Culture / Machine Learning

Autonomous Skiing Robots Compete for the Winter Olympics

8 Mar 2018 1:00am, by Kimberley Mok

Machine Learning

Mind-Reading AI Optimizes Images Reconstructed from Your Brain Waves

1 Mar 2018 12:00pm, by Kimberley Mok

API Management / Data / Machine Learning

The Future of Machine Learning

28 Feb 2018 2:30pm, by Swapnil Bhartiya

Data / Machine Learning / Security

Intezer Provides Code ‘DNA Mapping’ to Root out Malware

26 Feb 2018 10:21am, by Susan Hall

Development / Machine Learning

ARM’s Project Trillium Brings Dedicated Machine Learning Hardware to Smart Devices

22 Feb 2018 9:37am, by Mary Branscombe

Containers / Development / Kubernetes / Machine Learning

GitHub Predicts Hottest 2018 Open Source Trends

19 Feb 2018 8:58am, by Michelle Gienow

Development / Kubernetes / Machine Learning

This Week in Programming: All That Machine Learning Hullabaloo

17 Feb 2018 6:00am, by Mike Melanson

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