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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

The New Stack Context: Black Hat 2018 and New ML Workflow Tools

17 Aug 2018 12:57pm, by TNS Staff

Machine Learning

Add It Up: Data Scientists, Not Developers, Lead Machine Learning Efforts

17 Aug 2018 10:57am, by Lawrence E Hecht

Machine Learning

Decagon AI Predicts New And Dangerous Drug Interactions

16 Aug 2018 11:00am, by Kimberley Mok

Machine Learning

Salesforce Open Sources an Engine to Automate ML Model Building

16 Aug 2018 10:23am, by Joab Jackson

Machine Learning

Oracle Open Sources GraphPipe, a Network Protocol for Machine Learning Models

15 Aug 2018 10:02am, by Joab Jackson

Machine Learning

Nvidia Debuts AI-Ready Turing GPUs, with Real-Time Ray Tracing

14 Aug 2018 3:09pm, by Joab Jackson

Culture / Machine Learning

Cultural Bias in Artificial Intelligence

7 Aug 2018 11:03am, by TNS Staff

Culture / Edge / IoT / Machine Learning / Security

The Internet of Things: Securing Tomorrow’s Cars

6 Aug 2018 1:56pm, by Swapnil Bhartiya

Development / Machine Learning

Get Started with Google Cloud AutoML Vision for Image Classification

3 Aug 2018 9:21am, by Janakiram MSV

Machine Learning

MIT’s Blind Robot Overcomes Obstacles Without Sight

3 Aug 2018 3:00am, by Kimberley Mok

Culture / Machine Learning

Q&A: Google Cloud CEO Diane Greene on the Future of the Cloud

1 Aug 2018 9:46am, by Alex Handy

Data / Machine Learning / Contributed

The Role of Machine Learning in Data Management

1 Aug 2018 8:40am, by Srinivas Vadlamani

Kubernetes / Machine Learning

Build a Machine Learning Testbed Based on Kubernetes and Nvidia GPU

27 Jul 2018 6:00am, by Janakiram MSV

Machine Learning

Deep Learning Drone Detects Fights, Bombs, Shootings in Crowds

26 Jul 2018 11:28am, by Kimberley Mok

Data / Kubernetes / Machine Learning / Sponsored

Democratizing Machine Learning at Scale with Kubernetes on Azure

26 Jul 2018 6:02am, by Alex Williams and Mary Branscombe

Machine Learning / Contributed

Using Graphs and Machine Learning to Find Needles in a Haystack

19 Jul 2018 1:59pm, by Gaurav Deshpande

Machine Learning

Neo4j’s Emil Eifrem on Graph Databases and Machine Learning

18 Jul 2018 4:02pm, by TC Currie

Machine Learning

Machine Learning Made Easy

13 Jul 2018 10:11am, by Melissa Ng

Machine Learning

Train a Convolutional Neural Network with Nvidia DIGITS and Caffe

13 Jul 2018 6:00am, by Janakiram MSV

Machine Learning

Paperspace Co-Founders Discuss TPUs and Cloud Deep Learning

10 Jul 2018 1:43pm, by Alex Handy

Machine Learning / Contributed

Bayesian Inference: a Key Building Block of an AI Foundation

9 Jul 2018 3:00am, by Ebrahim Safavi

Culture / Machine Learning

IBM, Airbus Bring AI to the International Space Station

8 Jul 2018 6:00am, by David Cassel

Machine Learning

Nvidia DIGITS — An Easy Way to Get Started with Deep Learning

6 Jul 2018 9:00am, by Janakiram MSV

Data / Machine Learning / Microservices

This Week on The New Stack: Apache Spark and QCon

29 Jun 2018 3:00pm, by Libby Clark

Data / Machine Learning / Sponsored

The Good, Bad and Ugly: Apache Spark for Data Science Work

26 Jun 2018 1:07pm, by Robert Bennett

Kubernetes / Machine Learning

Nvidia Opens GPUs for AI Work with Containers, Kubernetes

26 Jun 2018 11:59am, by Janakiram MSV

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