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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 / Serverless

TWIMLcon: A New Conference to Help the Enterprise Adopt Machine Learning

5 Sep 2019 5:00pm, by TNS Staff

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CI/CD / Machine Learning / Monitoring / Sponsored / Contributed

The Big Picture of AIOps: Why You Need AI to Take Over DevOps

4 Sep 2019 9:27am, by Andreas Grabner

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Machine Learning

DeepMind’s Researchers Are Now Training Their AI By Using Soccer

2 Sep 2019 6:00am, by Kimberley Mok

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James Lovelock in 2005 by Bruno Comby via Wikipedia

Culture / Machine Learning

Why a 100-Year-Old Scientist Predicts AI Will Replace Humans

1 Sep 2019 6:00am, by David Cassel

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CI/CD / DevOps / Machine Learning / Sponsored / Contributed

How Your DevOps Can Build Confidence

21 Aug 2019 1:00pm, by Ravi Lachhman

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Machine Learning

What Is AIOps — And Why You Should Care

19 Aug 2019 12:00pm, by Kayla Matthews

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DevOps / Kubernetes / Machine Learning / Sponsored / Contributed

How AI Solves the Kubernetes Complexity Conundrum

15 Aug 2019 1:25pm, by Andreas Grabner

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Machine Learning

Scientists Developing Self-Healing Soft Robot That Can ‘Feel’ Pain

15 Aug 2019 12:00pm, by Kimberley Mok

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Data / Machine Learning

Splice Machine Brings SQL to Machine Learning

12 Aug 2019 3:45pm, by TC Currie

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Cloud Services / Machine Learning / Technology

Unravel Data Adds AI to Prevent Cloud-Migration Cost Hangovers

12 Aug 2019 6:00am, by Susan Hall

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Machine Learning / Contributed

3 New Techniques for Data-Dimensionality Reduction in Machine Learning

9 Aug 2019 12:00pm, by Rosaria Silipo and Maarit Widmann

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Data / Machine Learning

How Graph Databases are Changing Our Relationships with Data

7 Aug 2019 5:00pm, by Jennifer Riggins

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Culture / Machine Learning

Intel’s AI4 Social Good: Making the World a Better Place Through AI

31 Jul 2019 5:00pm, by TC Currie

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Machine Learning / Sponsored

Machine Learning Challenges Now More about Engineering than Research

30 Jul 2019 5:00pm, by B. Cameron Gain

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Machine Learning

Determined AI Promises End-to-End Orchestration and Model Management

29 Jul 2019 12:29pm, by Mary Branscombe

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Edge / IoT / Machine Learning / Networking

Kentik Turns AIOps Spotlight on Network Data, Workflows

29 Jul 2019 9:51am, by Susan Hall

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Machine Learning / Technology

Harvard’s New Open Source AI Algorithm Simplifies Protein Folding Puzzle

25 Jul 2019 12:00pm, by Kimberley Mok

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Culture / Machine Learning / Contributed

Removing Bias from AI Is a Human Endeavor

23 Jul 2019 9:10am, by Wilson Pang

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Development / Machine Learning / Technology

What Is Robotic Processing Automation?

22 Jul 2019 1:38pm, by Kayla Matthews

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Machine Learning

How Uber Eats Uses Machine Learning to Estimate Delivery Times

19 Jul 2019 10:17am, by Joab Jackson

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Machine Learning

Tutorial: Manage Machine Learning Lifecycle with Databricks MLflow

19 Jul 2019 3:00am, by Janakiram MSV

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DevOps / Machine Learning

How Artificial Intelligence and Machine Learning Are Disrupting DevOps

18 Jul 2019 5:00pm, by B. Cameron Gain

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Edge / IoT / Machine Learning

Elon Musk’s Neuralink Brain-Reading ‘Threads’ Will Be Robotically Implanted

18 Jul 2019 12:00pm, by Kimberley Mok

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Machine Learning / Technology

Mozilla BugBug Adds Machine Learning to Bugzilla

17 Jul 2019 6:30am, by TC Currie

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Machine Learning

AI Makes New Scientific Discoveries by Analyzing Old Research Papers

12 Jul 2019 12:53pm, by Kimberley Mok

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Edge / IoT / Machine Learning

Tutorial: Configure NVIDIA Jetson Nano as an AI Testbed

12 Jul 2019 11:14am, by Janakiram MSV

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