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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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CI/CD / Culture / Machine Learning / Sponsored

Supplant Scripting with Engineering Management and Machine Learning

5 Oct 2020 2:01pm, by Alex Williams and B. Cameron Gain

Edge / IoT / Kubernetes / Machine Learning

Tutorial: Configure, Deploy an Edge Application on Cloud Native Edge Infrastructure

5 Oct 2020 1:55pm, by Janakiram MSV

Machine Learning

AI-Powered ‘Prescriptive Analytics’ Could Reduce Hospitalization Rates, Cut Costs

25 Sep 2020 10:19am, by Kimberley Mok

Machine Learning / Contributed

Uncovering Biases: The Importance of Data Diversity in Speech Recognition

21 Sep 2020 12:00pm, by Scott Stephenson

Culture / Machine Learning / Contributed

5 Mistakes to Avoid with AIOps Projects

18 Sep 2020 12:00pm, by Deepak Jannu

Development / Machine Learning

These AI-Synthesized Sound Effects Are Realistic Enough to Fool Humans

6 Sep 2020 6:02am, by Kimberley Mok

Machine Learning / Technology

Tutorial: Deploying TensorFlow Models at the Edge with NVIDIA Jetson Nano and K3s

28 Aug 2020 9:07am, by Janakiram MSV

Development / Machine Learning

OpenAI’s GPT-3 Makes Big Leap Forward for Natural Language Processing

21 Aug 2020 7:33am, by Kimberley Mok

Data / Machine Learning / Contributed

Clean Data Is the Foundation of Effective Machine Learning

12 Aug 2020 11:16am, by Vikram Bahl

Development / Machine Learning

Swift’s Chris Lattner on the Possibility of Machine Learning-Enabled Compilers

9 Aug 2020 6:00am, by David Cassel

Machine Learning / Contributed

ML-Powered Predictive Analytics Can Be a Key to Maintaining Customer Satisfaction

7 Aug 2020 12:00pm, by Murali Krishnan

Development / Machine Learning / Tools / Contributed

Cohen’s Kappa: What It Is, When to Use It, and How to Avoid Its Pitfalls

4 Aug 2020 9:02am, by Maarit Widmann

Development / Machine Learning

DeText: LinkedIn’s Open Source Deep Learning Framework for Natural Language Processing

4 Aug 2020 8:46am, by Kimberley Mok

Development / Edge / IoT / Machine Learning

Tutorial: Accelerate AI at Edge with ONNX Runtime and Intel Neural Compute Stick 2

31 Jul 2020 10:00am, by Janakiram MSV

Machine Learning / Monitoring / Contributed

The Next Frontier for AIOps – Application Optimization

27 Jul 2020 4:00am, by Ofer Idan

Development / Machine Learning

Tutorial: Import an ONNX Model into TensorFlow for Inference

24 Jul 2020 9:22am, by Janakiram MSV

Machine Learning / Technology

AI Trounces Philosophers in Answering Philosophical Questions

23 Jul 2020 11:20am, by Kimberley Mok

Kubernetes / Machine Learning / Monitoring / Sponsored

How AI Observability Cuts Down Kubernetes Complexity

21 Jul 2020 5:00pm, by Richard MacManus

Culture / Machine Learning

Researchers Look at How ‘Algorithmic Coloniality’ May Hamper Artificial Intelligence

19 Jul 2020 6:00am, by David Cassel

Development / Machine Learning

Tutorial: Train a Deep Learning Model in PyTorch and Export It to ONNX

17 Jul 2020 8:51am, by Janakiram MSV

Data / Kubernetes / Machine Learning / Sponsored / Contributed

Volcano: A Kubernetes Native Batch System for AI, Big Data and HPC Workloads

13 Jul 2020 9:39am, by Anni Lai

Development / Machine Learning

Tutorial: Using a Pre-Trained ONNX Model for Inferencing

10 Jul 2020 10:21am, by Janakiram MSV

Culture / Machine Learning

Industry Facial Recognition AI Moratoriums Don’t Address Flaws, Privacy Concerns

10 Jul 2020 9:21am, by Kimberley Mok

Machine Learning / Technology

Open Neural Network Exchange Brings Interoperability to Machine Learning Frameworks

9 Jul 2020 1:56pm, by Janakiram MSV

Cloud Native / Data / Machine Learning

Cockroach Labs and the Scalable Power of Distributed SQL

6 Jul 2020 6:00am, by Richard MacManus

Machine Learning / Technology / Contributed

The Promising Duo: Five Use Cases for Natural Language Processing in FinTech

29 Jun 2020 11:33am, by Yana Yelina and Oksana Mikhalchuk

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