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DevOps Follow 972 Followers

Electric Cloud Launches Predictive Analytics for DevOps

by Helen Beal Follow 4 Followers on  Jul 03, 2018

ElectricFlow DevOps Foresight uses deep learning to identify patterns in release pipelines, gauge the likelihood of software release success and make recommendations in order to incrementally improve pipeline performance and application quality.

AI, ML & Data Engineering Follow 1002 Followers

Apple Has Released Core ML 2

by Roland Meertens Follow 8 Followers on  Jun 07, 2018

At WWDC Apple released Core ML 2: a new version of their machine learning SDK for iOS devices. The new release of Core ML should create an inference time speedup of 30% for apps developed using Core ML 2. An important new feature of the Core ML SDK is Create ML. Developers can create and train custom machine learning models on their mac.

AI, ML & Data Engineering Follow 1002 Followers

Q&A on IBM's Fabric for Deep Learning with Chief Architect of Watson

by Rags Srinivas Follow 11 Followers on  Apr 30, 2018

InfoQ caught up with Ruchir Puri, chief architect of Watson, regarding the Fabric for Deep Learning (FfDL).

AI, ML & Data Engineering Follow 1002 Followers

Tensorflow with Javascript Brings Deep Learning to the Browser

by Alexis Perrier Follow 1 Followers on  Apr 18, 2018

Google launched Tensorflow.js, a Javascript implementation of its open-source Tensorflow deep-learning framework during the recent TensorFlow Dev Summit 2018. Tensorflow.js enables training models directly in the browser by leveraging the WebGL JavaScript API for faster computations.

AI, ML & Data Engineering Follow 1002 Followers

Dataiku's Latest Release Integrates Deep-Learning for Computer Vision

by Alexis Perrier Follow 1 Followers on  Apr 11, 2018

Collaborative data science platform Dataiku's latest release of its Data Science Studio includes pre-trained deep learning models for image processing. The DSS platform implements each step of a data-science project from data-sourcing and visualization to production deployment. Its machine-learning module supports standard libraries and it integrates with Hadoop and multiple Spark engines.

DevOps Follow 972 Followers

How Booking.com Uses Kubernetes for Machine Learning

by Manuel Pais Follow 9 Followers on  Apr 01, 2018

Sahil Dua explained how Booking.com was able to scale machine learning (ML) models for recommending destinations and accommodation to their customers using Kubernetes, at the QCon London conference. In particular, he stressed how Kubernetes elasticity and resource starvation avoidance on containers helps them run computationally (and data) intensive, hard to parallelize, machine learning models.

AI, ML & Data Engineering Follow 1002 Followers

Microsoft Embeds Artificial Intelligence Platform in Windows 10 Update

by Alexis Perrier Follow 1 Followers on  Mar 20, 2018

The next Windows 10 update opens the way for the integration of artificial intelligence functionalities within Windows applications. Developers will be able to integrate pre-trained deep-learning models converted to the ONNX framework in their Windows applications.

AI, ML & Data Engineering Follow 1002 Followers

Facebook Releases Open Source "Detectron" Deep-Learning Library for Object Detection

by Alexis Perrier Follow 1 Followers on  Mar 13, 2018

Recent releases from Facebook and Google implement the most current deep-learning algorithms to take a crack at the challenging problem of machine object detection.

AI, ML & Data Engineering Follow 1002 Followers

Autonomous Vehicles Became Better at Predicting Lane-Changes

by Roland Meertens Follow 8 Followers on  Feb 07, 2018

Researchers created an algorithm that allows self-driving cars to predict lane-changes of the surrounding cars. The system works by using a deep-learning technique called Long Short-Term Memories (LSTMs). Although the most likely scenario on the highway is that every car stays in its own lane, their algorithm was able to slightly improve on this baseline prediction.

AI, ML & Data Engineering Follow 1002 Followers

Q&A on Machine Learning and Kubernetes with David Aronchick of Google from Kubecon 2017

by Rags Srinivas Follow 11 Followers on  Jan 24, 2018

InfoQ caught up with David Aronchick, product manager at Google and contributor to Kubeflow about the synergy between Kubernetes and Machine Learning at Kubecon 2017.

AI, ML & Data Engineering Follow 1002 Followers

Machine Learning and Artificial Intelligence - Two Conferences to Attend in 2018

by Wesley Reisz Follow 17 Followers on  Jan 19, 2018

The IEEE publishes an annual list of the Top 10 Technology Trends for each upcoming year. Making the list for 2018 are multiple topics surrounding artificial intelligence and machine learning. Deep learning comes in as the IEEE hottest trend for 2018.

AI, ML & Data Engineering Follow 1002 Followers

Modern Big Data Pipelines over Kubernetes

by Srini Penchikala Follow 38 Followers on  Jan 08, 2018

Container management technologies like Kubernetes make it possible to implement modern big data pipelines. Eliran Bivas, senior big data architect at Iguazio, spoke at the recent KubeCon + CloudNativeCon North America 2017 Conference about big data pipelines and how Kubernetes can help develop them.

AI, ML & Data Engineering Follow 1002 Followers

Building GPU Accelerated Workflows with TensorFlow and Kubernetes

by Srini Penchikala Follow 38 Followers on  Jan 04, 2018

Daniel Whitenack spoke at the recent KubeCon + CloudNativeCon North America 2017 Conference about GPU based deep learning workflows using TensorFlow and Kubernetes technologies. He discussed the open source data pipeline framework Pachyderm.

AI, ML & Data Engineering Follow 1002 Followers

How Apple Does Realtime Recognition of Handwritten Chinese Characters

by Dylan Raithel Follow 8 Followers on  Dec 20, 2017

Apple details building on-device handwritten Chinese character recognition with convolutional-neural networks and image recognition.

AI, ML & Data Engineering Follow 1002 Followers

Panel on the Future of AI

by Michael Stiefel Follow 6 Followers on  Dec 15, 2017

An SF QCon panel on the future of AI explored some issues facing machine learning today. The areas explored: critical issues facing AI right now, how has technology changed the way people are hired, how non-leading edge companies make the best use of current technologies, what the role of humans in relation to AI is, and exciting new breakthroughs on the immediate horizon.

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