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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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Culture / Edge / IoT / Machine Learning / Technology

Headless Robot Cats, Parallel Realities: CES 2020 and the Shape of Things to Come

12 Jan 2020 6:00am, by David Cassel

Machine Learning / Open Source

Deep Learning AI Tool Identifies ‘Fake News’ with Automated Fact Checking

3 Jan 2020 9:00am, by Kimberley Mok

CI/CD / Machine Learning

Add It Up: How Long Does a Machine Learning Deployment Take?

19 Dec 2019 10:33am, by Lawrence E Hecht

Machine Learning / Technology

Check Your ML Carbon Footprint with the Machine Learning Emissions Calculator

13 Dec 2019 10:46am, by Kimberley Mok

Cloud Services / Machine Learning

AWS Launches an IDE to Manage the Full Machine Learning Lifecycle

3 Dec 2019 2:08pm, by Joab Jackson

mannequin head

Culture / Data / Machine Learning

Threads and Threats When Computers Think and Biases Emerge

21 Nov 2019 5:30pm, by Alex Williams

Machine Learning

Researchers Build an ‘Interpretable’ AI That Shows How It Thinks

21 Nov 2019 11:00am, by Kimberley Mok

Machine Learning

Researchers Redesign Neural Networks for Continuously Changing Data

15 Nov 2019 10:11am, by Kimberley Mok

Cloud Services / Data / Machine Learning / Contributed

Getting Cloud Data Lakes Right

13 Nov 2019 8:39am, by Prateek Shrivastava and Ranga Chandrasekaran

Data / Edge / IoT / Machine Learning

The Challenge of Machine Learning and How DevOps and the Edge Will Modernize Data Science

11 Nov 2019 5:00pm, by Jennifer Riggins

Culture / Data / Machine Learning / Contributed

4 Keys to Navigating the AI/ML Modernization Journey

5 Nov 2019 9:04am, by Kristin Simonini

Data / Machine Learning

Apache Ignite Machine Learning: Computing in Place at Scale

4 Nov 2019 10:13am, by Susan Hall

Machine Learning

LOGAN Is a Deep Learning AI That Transforms 3D Shapes Seamlessly

1 Nov 2019 2:08pm, by Kimberley Mok

Machine Learning / Technology

MIT’s New AI Tackles Loopholes in ‘Fake News’ Detection Tools

25 Oct 2019 12:22pm, by Kimberley Mok

Culture / Machine Learning

Why Can’t AI Beat Humans at Angry Birds?

20 Oct 2019 6:00am, by David Cassel

Machine Learning

Researchers Use AI to Create Super-Compressible Meta-Material

18 Oct 2019 1:00pm, by Kimberley Mok

Linux / Machine Learning / Open Source / Storage / Technology

Ubuntu 19.10 Promises an Improved Experience for AI/ML Developers

18 Oct 2019 10:26am, by Jack Wallen

Machine Learning / Monitoring

OpsRamp Takes AIOps to Hybrid Environments

14 Oct 2019 11:32am, by Susan Hall

Machine Learning

DeepPrivacy AI Uses Deepfake Tech to Anonymize Faces and Protect Privacy

11 Oct 2019 11:05am, by Kimberley Mok

Edge / IoT / Machine Learning

How I Built an ‘AIoT’ Project with Intel AI Vision X Developer Kit and Arduino Yun

11 Oct 2019 10:06am, by Janakiram MSV

Culture / Machine Learning

AI Weirdness Comes to ‘Inktober’

6 Oct 2019 6:00am, by David Cassel

Machine Learning

Intel OpenVINO Brings AI Inferencing to the Desktop and Edge

4 Oct 2019 10:18am, by Janakiram MSV

Machine Learning

AI Surprises Researchers by Inventing New Hide-and-Seek Strategies

3 Oct 2019 12:53pm, by Kimberley Mok

CI/CD / Development / Machine Learning / Sponsored / Contributed

Robotic Process Automation: A New Spin on Old Challenges

1 Oct 2019 8:56am, by Wayne Ariola

Data / Kubernetes / Machine Learning

Big Data: Google Replaces YARN with Kubernetes to Schedule Apache Spark

23 Sep 2019 11:44am, by Susan Hall

Machine Learning

A Closer Look at Microsoft Vision AI Kit

20 Sep 2019 11:14am, by Janakiram MSV

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