SEARCH (ENTER TO SEE ALL RESULTS)

POPULAR TOPICS

Analysis
News
Contributed
The New Stack Makers
Open Source
Research
Tutorial
Science
Off-The-Shelf Hacker
API Management
Skip to content
  • Ebooks
    • Storage
    • DevOps
    • Serverless
    • Microservices
    • Kubernetes Ecosystem
    • Docker Ecosystem
    • All Ebooks
  • Podcasts
    • TNS @Scale Series
    • TNS Analysts Round Table
    • TNS Context Weekly News
    • TNS Makers Interviews
    • All Podcasts
  • Events
  • Newsletter
  • • • •
    • Ebooks
      • Machine Learning
      • DevOps
      • Serverless
      • Microservices
      • Kubernetes Ecosystem
      • Docker Ecosystem
      • All Ebooks
    • Podcasts
      • TNS @Scale Series
      • TNS Analysts Round Table
      • TNS Context Weekly News
      • TNS Makers Interviews
      • All Podcasts
    • Events
    • Newsletter
Skip to content
  • Architecture
    • Cloud Native
    • Containers
    • Edge/IoT
    • Microservices
    • Networking
    • Serverless
    • Storage
  • Development
    • Security
    • Cloud Services
    • Data
    • Machine Learning
    • Development
  • Operations
    • CI/CD
    • Culture
    • DevOps
    • Kubernetes
    • Monitoring
    • Service Mesh
    • Tools
 

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.

+

Development / Machine Learning

With Innovative Gaming Moves, Google’s AI Becomes Go Grandmaster in 3 Days

2 Nov 2017 11:20am, by Kimberley Mok

+

Data / Development / Machine Learning

Google Firebase Gets Crashlytics, A/B Testing, AI-Based Predictive Modeling

1 Nov 2017 5:00am, by Michelle Gienow

+

Development / Machine Learning

Off-The-Shelf Hacker: Machine Vision Meets the Robotic Skull

1 Nov 2017 2:00am, by drtorq

+

Data / Development / Machine Learning / Tools

This Week in Programming: Do You Need a Blockchain?

28 Oct 2017 6:00am, by Mike Melanson

+

Machine Learning

How SigOpt Wants to Optimize Your Machine Learning Algorithms

27 Oct 2017 6:00am, by Kimberley Mok

+

Data / Development / Machine Learning / Contributed

Building a Machine Learning Application? Start with SQL

25 Oct 2017 6:00am, by Adam Prout

+
Larry Wall (smiling) on Perl 6 - March 2017

Culture / Development / Edge / IoT / Machine Learning

Larry Wall’s Quest for a 100-Year Programming Language

22 Oct 2017 6:00am, by David Cassel

+

Culture / Machine Learning

Move Fast and Break People: ‘Technically Wrong’ Examines Toxic Tech and What to Do about It

20 Oct 2017 6:45am, by TC Currie

+

Cloud Native / Machine Learning / Contributed

Does Artificial Intelligence Require Specialized Processors?

20 Oct 2017 3:00am, by Massimiliano Versace

+

Containers / Culture / Data / Edge / IoT / Machine Learning / Serverless

Azure IoT Edge, Machine Learning and Containers

17 Oct 2017 2:00am, by Mary Branscombe

+

Data / Machine Learning / Contributed

Together, Metadata and Machine Learning Can Help Automate Data Integration

12 Oct 2017 8:00am, by Lakshmi Randall

+

Culture / Edge / IoT / Kubernetes / Machine Learning

Swarmanoids: Modular Robots that Assemble to Form Artificial Nervous Systems

12 Oct 2017 6:00am, by Kimberley Mok

+

Culture / Machine Learning / Contributed

Deep Learning Dissected: The Awesome Potential of Artificial Intelligence

9 Oct 2017 9:00am, by Adel El-Hallak

+

CI/CD / Data / Development / Machine Learning / Serverless

This Week in Programming: Oracle, Lawnmowers, and Open Source

7 Oct 2017 6:00am, by Mike Melanson

+

Machine Learning

A Geek’s Rap about Neural Networks

30 Sep 2017 9:00am, by Alex Williams

+

Culture / Machine Learning

Bodega’s Glorified Vending Machine as ‘Corner Store’ Idea Is a Cautionary Tale

29 Sep 2017 12:00pm, by Kimberley Mok

+

Machine Learning / Sponsored

The Art of Building Neural Networks

27 Sep 2017 1:59pm, by Alex Williams

+

Machine Learning / Security / Contributed

Machine Learning To Help Find Anomalous and Malicious Activity

26 Sep 2017 7:00am, by Rohan Tandon

+

Development / Machine Learning

Facebook, Microsoft Bring Interoperable Models to Machine Learning Toolkits

25 Sep 2017 2:00am, by Mary Branscombe

+

Cloud Services / Machine Learning / Storage

DigitalOcean Adds Object Storage and Machine Learning

20 Sep 2017 12:37pm, by TC Currie

+

Cloud Native / Containers / Kubernetes / Machine Learning

Uber Devises a Scheduler to Run TensorFlow Deep Learning Jobs Across Multiple GPUs

19 Sep 2017 12:12pm, by Joab Jackson

+

CI/CD / Development / DevOps / Machine Learning / Sponsored

PagerDuty CEO on Advancing DevOps

18 Sep 2017 2:30pm, by Scott M. Fulton III

+

Development / Machine Learning

This Week in Programming: Small Changes that Make a Big Difference

16 Sep 2017 6:00am, by Mike Melanson

+

Data / DevOps / Machine Learning / Monitoring

New Relic FutureStack17: Instrumentation and Intelligence Feed the Need for Speed

15 Sep 2017 7:00am, by TC Currie

+

Machine Learning

Camouflaged Graffiti on Road Signs Can Fool Machine Learning Models

14 Sep 2017 11:00am, by Kimberley Mok

+

Development / Machine Learning / Security

Machine Learning Lends a Hand for Automated Software Testing

13 Sep 2017 2:00am, by Mary Branscombe

11 12 13 14 15 16 17 18 19 20

Architecture

  • Cloud Native
  • Containers
  • Edge/IoT
  • Microservices
  • Networking
  • Serverless
  • Storage

Development

  • Security
  • Cloud Services
  • Data
  • Machine Learning
  • Development

Operations

  • CI/CD
  • Culture
  • DevOps
  • Kubernetes
  • Monitoring
  • Service Mesh
  • Tools

The New Stack

  • Ebooks
  • Podcasts
  • Events
  • Newsletter
  • About / Contact
  • Sponsors
  • Disclosures
  • Contributions
  • Twitter
  • Facebook
  • YouTube
  • Soundcloud
  • LinkedIn
  • Slideshare
  • RSS

© 2019 The New Stack. All rights reserved.

Privacy Policy. Terms of Use.