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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 / Contributed

The Keys to the Future of AI Democratization

18 Dec 2018 10:23am, by Laurent Bride

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Chess Set

Machine Learning

DeepMind’s New Milestones on the Road to Artificial General Intelligence

16 Dec 2018 6:00am, by David Cassel

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Machine Learning / Technology

DeepMind AI Makes Breakthrough with ‘Protein Folding Problem’

13 Dec 2018 12:51pm, by Kimberley Mok

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Culture / Edge / IoT / Machine Learning

Meet ‘Yanu,’ an AI-Powered, Cloud-Based Robot Bartender

2 Dec 2018 6:00am, by David Cassel

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Machine Learning

Facial Recognition AIgorithm Re-Trained to Recognize Faraway Galaxies

30 Nov 2018 11:00am, by Kimberley Mok

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Machine Learning

How AutoML Puts the Power of AI in the Hands of Business Analysts

30 Nov 2018 3:00am, by Janakiram MSV

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Kubernetes / Machine Learning / Sponsored

Kubeflow Co-Founder: Machine Learning Workflows on Kubernetes Can Be Simple

28 Nov 2018 2:06pm, by B. Cameron Gain

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Cloud Services / Edge / IoT / Machine Learning / Serverless

AWS Re:Invent: New Machine Learning, Data, Infrastructure Services

28 Nov 2018 1:12pm, by Joab Jackson

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Machine Learning / Contributed

How Engineering Teams Will Evolve for Real-Time Product Data

23 Nov 2018 6:00am, by Derek Choy

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Cloud Native / Machine Learning / Sponsored

Telus Takes First Step Toward AI/ML with IT Automation

22 Nov 2018 5:00am, by Libby Clark

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Machine Learning

Meet FloydHub: The Heroku of Data Science

21 Nov 2018 3:00am, by Janakiram MSV

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CI/CD / Cloud Native / Cloud Services / Edge / IoT / Machine Learning

All The Hot Infrastructure Tech at OpenStack Summit Berlin

16 Nov 2018 10:15am, by Joab Jackson

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Machine Learning

MIT Algorithm Sniffs Out Sites Dedicated to ‘Fake News’

15 Nov 2018 3:00pm, by Kimberley Mok

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Machine Learning / Networking

Machine Learning for Operations

14 Nov 2018 3:00am, by Mary Branscombe

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Machine Learning

Build Machine Learning Models with IBM Watson Studio Cloud

9 Nov 2018 8:28am, by Janakiram MSV

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Machine Learning / Technology

Train, Deploy Machine Learning Models with Amazon SageMaker

2 Nov 2018 3:00am, by Janakiram MSV

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Machine Learning / Technology

Google Cloud ML Engine: Train, and Deploy Machine Learning Models

26 Oct 2018 3:00am, by Janakiram MSV

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Machine Learning / Technology

CA’s AIOps Platform Promises Holistic Intelligence for Operations

23 Oct 2018 3:00am, by Susan Hall

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Machine Learning

AI Automates Video Game Design With ‘Conceptual Expansion’

18 Oct 2018 11:00am, by Kimberley Mok

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Data / Machine Learning / Technology

How to Solve the Performance Challenges of Web-Scale Applications

18 Oct 2018 9:12am, by Nikita Ivanov

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Edge / IoT / Machine Learning

Bring Intelligence to the Edge with Custom AI Chips

12 Oct 2018 9:48am, by Janakiram MSV

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Kubernetes / Machine Learning

Use Kubernetes to Speed Machine Learning Development

12 Oct 2018 8:25am, by Justin Brandenburg

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Machine Learning

Humans in the Loop: How to Harness the Power of the Crowd

10 Oct 2018 3:00am, by Charly Walther

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Containers / Machine Learning

How Azure ML Streamlines Cloud-based Machine Learning

5 Oct 2018 8:56am, by Janakiram MSV

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Machine Learning

Harvard and Google’s AI Predicts Earthquake Aftershocks

20 Sep 2018 12:22pm, by Kimberley Mok

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Machine Learning

Add It Up: Machine Learning Developers Don’t Predict

20 Sep 2018 10:12am, by Lawrence E Hecht

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