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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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Development / Machine Learning

Avocado Chairs at the Intersection of Human Language and Neural Networks

22 Jan 2021 10:00am, by Kimberley Mok

Culture / DevOps / Machine Learning / Contributed

Meet the Star Member of the IT Team: The AI Assistant

15 Jan 2021 1:07pm, by Bob Friday

Cloud Services / Data / Machine Learning

Software Engineers Use Spreadsheets; Data Engineers Use the Cloud

14 Jan 2021 1:00pm, by Lawrence E Hecht

CI/CD / Machine Learning / Sponsored / Contributed

4 Ways AI Will Shape CI/CD in 2021

11 Jan 2021 8:30am, by Tiffany Jachja

Development / Edge / IoT / Machine Learning

MIT Machine Learning Uses ‘Graph Grammar’ to Automate and Optimize Robot Design

6 Jan 2021 12:20pm, by Kimberley Mok

Data / Machine Learning / Sponsored / Contributed

Data Engineer Emerges as the Critical Role for Data Success

5 Jan 2021 7:00am, by Nick Heudecker

Culture / Machine Learning

The Year in AI: What’s Behind in 2020, and What’s Ahead

31 Dec 2020 7:00am, by Kimberley Mok

Data / Machine Learning / Contributed

The Battle Between Unsupervised and Supervised AI

31 Dec 2020 6:00am, by Kim del Fierro

Machine Learning / Technology

Apache TVM: Portable Machine Learning Across Backends

28 Dec 2020 6:00am, by Susan Hall

Cloud Native / Cloud Services / Machine Learning

Amazon Web Services Takes the Silicon Wars to the Cloud

16 Dec 2020 9:32am, by B. Cameron Gain

Culture / Development / Machine Learning

Glassdoor: Don’t Sacrifice Performance for New Features

14 Dec 2020 3:00pm, by Alex Williams

Cloud Services / Edge / IoT / Machine Learning

How AWS Panorama Accelerates Computer Vision at the Edge

11 Dec 2020 9:34am, by Janakiram MSV

Data / DevOps / Machine Learning

Fiddler Drills into the Decisions Behind AI Decision-Making

10 Dec 2020 5:00am, by Susan Hall

Cloud Services / Data / Machine Learning

Amazon Web Services Brings Machine Learning to DataOps

9 Dec 2020 2:08pm, by Joab Jackson and B. Cameron Gain

Development / Machine Learning / Contributed

How Transfer Learning Can Make Machine Learning More Efficient

4 Dec 2020 12:30pm, by Mark Kurtz

Culture / Development / Machine Learning

This AI Can Automatically Decipher Lost Ancient Languages

4 Dec 2020 12:00pm, by Kimberley Mok

Data / Development / Machine Learning

TerminusDB Takes on Data Collaboration with a git-Like Approach

1 Dec 2020 8:00am, by Susan Hall

Data / Machine Learning / Security / Sponsored

Microsoft: Machine Learning Models Can Be Easily Reverse Engineered

30 Nov 2020 11:45am, by Joab Jackson

Machine Learning / Monitoring / Sponsored / Contributed

How to Get Started with AIOps

18 Nov 2020 1:00pm, by Annette Sheppard

Data / Machine Learning

Q&A: Bridging Data and ML Models with Feast, the Open Source Feature Store

16 Nov 2020 12:00pm, by Kimberley Mok

Cloud Services / Machine Learning / Serverless

Tutorial: Host a Serverless ML Inference API with AWS Lambda and Amazon EFS

5 Nov 2020 12:42pm, by Janakiram MSV

Cloud Services / Machine Learning / Serverless

Tutorial: Host a PyTorch Model for Inference on an Amazon EC2 Instance

4 Nov 2020 10:45am, by Janakiram MSV

Cloud Native / Machine Learning / Security

Snyk Rethinks Static Application Security Testing for Developers

29 Oct 2020 12:20pm, by B. Cameron Gain

Culture / Machine Learning

AI Corrects 50 Years of Sex Bias in Drug Safety Trials

23 Oct 2020 10:43am, by Kimberley Mok

Edge / IoT / Machine Learning / Networking

NVidia’s Planned Acquisition of Arm Portends Radical Data Center Changes

13 Oct 2020 10:16am, by Mary Branscombe

Data / Development / Machine Learning / Contributed

Numeric Scoring Metrics: Find the Right Metric for a Prediction Model

9 Oct 2020 12:42pm, by Maarit Widmann

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