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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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Data / Kubernetes / Machine Learning

Illuminating the Anonymous with Neo4j’s Graph Database

29 Jun 2020 6:00am, by Richard MacManus

Cloud Services / Edge / IoT / Machine Learning

Farming-as-a-Service: Could Robots Feed the World?

28 Jun 2020 6:00am, by David Cassel

DevOps / Machine Learning / Security / Technology

GuardRails: Security for the DevOps Age

25 Jun 2020 1:00pm, by Susan Hall

Data / Development / Machine Learning

Building a Lakehouse with Databricks and Machine Learning

22 Jun 2020 6:00am, by Richard MacManus

Culture / Machine Learning

Clearview’s Controversial Facial Recognition AI Automates Mass Surveillance

19 Jun 2020 11:02am, by Kimberley Mok

Data / DevOps / Machine Learning

DataStax Vector Provides Warnings Before a Cassandra Disaster May Strike

18 Jun 2020 8:46am, by B. Cameron Gain

Data / Development / Machine Learning / Contributed

Machine Learning in Production: Lessons Learned from Deploying Our First ML Model

17 Jun 2020 11:10am, by Alex Post

Machine Learning

The Moral Choice Machine: An AI that Learns ‘Right’ from ‘Wrong’

8 Jun 2020 11:35am, by Kimberley Mok

Data / Development / Machine Learning / Contributed

Socialize Your Data? Why DataOps Improves Data Ethics

3 Jun 2020 11:16am, by Antonios Chalkiopoulos

Machine Learning / Technology

Google’s AutoML-Zero Evolves Machine Learning Algorithms Using Basic Math

29 May 2020 11:51am, by Kimberley Mok

Machine Learning

Tutorial: Train Machine Learning Models with Automated ML Feature of Azure ML

29 May 2020 11:00am, by Janakiram MSV

Machine Learning / Contributed

Stages of a Human-in-the-Loop Machine Learning Application

26 May 2020 12:00pm, by Bernat Fages

Data / Machine Learning / Open Source

RedisConf 2020: Why Redis Is More Than Just a Cache Provider

18 May 2020 3:00am, by B. Cameron Gain

Machine Learning / Monitoring / Contributed

Observability and the Misleading Promise of AIOps

15 May 2020 11:24am, by Danyel Fisher

Machine Learning / Technology

Researchers Prove Some Commonly Used AI Models Don’t Work as Intended

15 May 2020 10:03am, by Kimberley Mok

Cloud Services / Machine Learning

Tutorial: Create Training and Inferencing Pipelines with Azure ML Designer

15 May 2020 8:31am, by Janakiram MSV

Data / Development / Machine Learning / Contributed

How Solving the Multi-Armed Bandit Problem Can Move Machine Learning Forward

13 May 2020 3:00am, by Dattaraj Rao

Machine Learning / Contributed

Demystifying Deep Learning and Artificial Intelligence

8 May 2020 8:53am, by Levon Paradzhanyan

Cloud Services / Kubernetes / Machine Learning

Build Repeatable ML Workflows with Azure Machine Learning Pipelines

8 May 2020 8:27am, by Janakiram MSV

DevOps / Machine Learning / Security / Contributed

AIOps Readiness in 5 Steps

6 May 2020 9:34am, by Paul Scully

Data / Machine Learning / Monitoring

Databand: Observability for Data Pipelines

27 Apr 2020 12:17pm, by Susan Hall

Cloud Native / Data / Machine Learning

The 2020s Will Be Defined by Scale-Out Data

22 Apr 2020 5:00pm, by Richard MacManus

CI/CD / DevOps / Machine Learning

Could AIOps Play a Role in the Future of IT Operations?

22 Apr 2020 7:41am, by Jennifer Riggins

Data / Machine Learning / Contributed

Demystifying Machine Learning: How ML Discovers New Information

22 Apr 2020 3:00am, by Levon Paradzhanyan

Machine Learning / Monitoring / Networking

New Relic’s Ambitious Plan to Apply AI and ML to Incident Response

16 Apr 2020 1:37pm, by B. Cameron Gain

Data / Development / Machine Learning

Data Sharing Key to Success for COVID-19 Data Models

16 Apr 2020 7:26am, by Lawrence E Hecht

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