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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 / Monitoring

AIOps Users Found in the Wild

2 May 2019 9:00am, by Lawrence E Hecht

Machine Learning

MIT and Yale’s RoCycle Robot Can Sort Recyclables by ‘Feeling’ Them

29 Apr 2019 11:02am, by Kimberley Mok

Cloud Services / Machine Learning

Machine Learning for Drug Discovery Using the Google Kubernetes Engine

23 Apr 2019 3:00am, by Emily Omier

Kubernetes / Machine Learning / Sponsored

Context: Apache Spark for Artificial Intelligence and AI 2.0

19 Apr 2019 3:00pm, by Libby Clark

Data / Machine Learning

Why the Self-Adapting Data Warehouse Is the Future

19 Apr 2019 9:51am, by Kent Graziano

Machine Learning

MIT’s Proxyless Algorithm Optimizes and Automates AI Design

19 Apr 2019 8:47am, by Kimberley Mok

Machine Learning / Contributed

Deep Learning Broadens the Reach of Artificial Intelligence

17 Apr 2019 12:15pm, by Tareq Aljaber

Culture / Edge / IoT / Machine Learning

Off-The-Shelf Hacker: Robotic Baby Steps

13 Apr 2019 6:00am, by drtorq

Machine Learning

TossingBot Uses ‘Residual Physics’ to Chuck Like a Champ

12 Apr 2019 11:00am, by Kimberley Mok

DevOps / Machine Learning / Sponsored

Machine Learning Finds Its Place in the Production Pipeline

10 Apr 2019 5:00pm, by B. Cameron Gain

DevOps / Machine Learning / Networking / Technology / Contributed

How AIOps Supports a DevOps World

8 Apr 2019 12:00pm, by Jiayi Hoffman

Machine Learning

Q&A: Dialpad’s Etienne Manderscheid on the Power of Voice AI

4 Apr 2019 12:00pm, by Kimberley Mok

Data / Kubernetes / Machine Learning

MapR Brings Apache Spark and Apache Drill to Kubernetes

2 Apr 2019 9:00am, by Mike Melanson

Edge / IoT / Machine Learning / Technology

MIT Robot Uses Tactile Reasoning AI to Play Jenga Like a Human

28 Mar 2019 6:00pm, by Kimberley Mok

Development / Machine Learning

Quantum Computing’s Challenging Liftoff to Commercialization

28 Mar 2019 1:00pm, by Joab Jackson

Culture / Data / Machine Learning

The Neural Network that Generated Thousands of Technical Questions

24 Mar 2019 9:39am, by David Cassel

Machine Learning

Aible: AI That Speaks Business

21 Mar 2019 11:06am, by Susan Hall

Kubernetes / Machine Learning

How Kubernetes Could Orchestrate Machine Learning Pipelines

20 Mar 2019 1:34pm, by Mary Branscombe

Data / Machine Learning

MapR Executive on Operational AI: Logistics First, Algorithms Later

19 Mar 2019 5:00pm, by TC Currie

Machine Learning / Security / Contributed

The Possibilities of AI and Machine Learning for Cybersecurity

19 Mar 2019 9:33am, by Juned Ghanchi

Edge / IoT / Machine Learning

Tutorial: Accelerate Deep Learning Models with Intel Movidius

15 Mar 2019 8:30am, by Janakiram MSV

Kubernetes / Machine Learning / Technology

Primer: Kubeflow Streamlines Machine Learning with Kubernetes

11 Mar 2019 11:51am, by Emily Omier

Culture / Machine Learning

How AI Will Help Us Find New, Innovative Flavors of the Future

8 Mar 2019 3:00pm, by Kimberley Mok

Edge / IoT / Machine Learning

A Closer Look at Intel Movidius Neural Compute Stick

8 Mar 2019 9:50am, by Janakiram MSV

Machine Learning

How Machine Learning Pipelines Work and What Needs Improving

5 Mar 2019 7:00am, by Mary Branscombe

DevOps / Kubernetes / Machine Learning

Machine Learning, Microservices, and Kubernetes

28 Feb 2019 3:00pm, by B. Cameron Gain

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