Cloud weekly news digest (6.1.2019)

January 6th, 2019
Cloud weekly news digest (6.1.2019)

Deep Learning / AI: Practical AI for the Working Software Engineer on Azure: https://notebooks.azure.com/davidsmi/projects/practicalai https://github.com/revodavid/PracticalAI/blob/master/AIF01%20Practical%20AI%20for%20the%20Working%20Software%20Engineer.pdf Getting Started with Deep Learning on Azure: https://github.com/revodavid/PracticalAI/blob/master/AIH07%20Getting%20Started%20with%20Deep%20Learning.pdf Migrating & Scaling Machine Learning Models to Azure Databricks for Cloud-Powered AI: https://www.blue-granite.com/blog/migrating-scaling-machine-learning-models-azure-databricks-cloud-powered-ai Data Science: Ten Myths About Data Science: https://www.dataversity.net/ten-myths-about-data-science/ Data Science Trends in 2019: https://www.dataversity.net/data-science-trends-in-2019/ Data Analytics: 3 Common Analytics

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Cloud weekly news digest (23.12.2018)

December 23rd, 2018
Cloud weekly news digest (23.12.2018)

Machine Learning: Accelerate ML Training on Amazon SageMaker Using GPU-Based EC2 P3 Instances https://www.slideshare.net/AmazonWebServices/accelerate-ml-training-on-amazon-sagemaker-using-gpubased-ec2-p3-instances-cmp413r1-aws-reinvent-2018 Easily train models using datasets labeled by Amazon SageMaker Ground Truth https://aws.amazon.com/blogs/machine-learning/easily-train-models-using-datasets-labeled-by-amazon-sagemaker-ground-truth/ Train PyTorch models with Azure Machine Learning service https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-train-pytorch Scalable multi-node training with TensorFlow https://aws.amazon.com/blogs/machine-learning/scalable-multi-node-training-with-tensorflow/ Train TensorFlow models with Azure Machine Learning service https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-train-tensorflow ML Best Practices: Prepare Data,

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Cloud Event for Researchers

December 17th, 2018

Cloud Workshop (in Hebrew) designed especially for researchers will take place on December 30, 2018 at Kfar Hamacabia. More information.

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Cloud weekly news digest (16.12.2018)

December 16th, 2018
Cloud weekly news digest (16.12.2018)

Machine Learning: Google cloud now offers Cloud TPU Pods – tightly-coupled Tensor Processing Unit (TPU) chips linked via an ultrafast custom interconnect (currently in alpha stage) https://cloud.google.com/blog/products/ai-machine-learning/now-you-can-train-ml-models-faster-and-lower-cost-cloud-tpu-pods MLPerf v0.5 – comparison of machine learning hardware manufactures: https://mlperf.org/results/ https://github.com/mlperf/policies/blob/master/training_summary.adoc https://cloud.google.com/blog/products/ai-machine-learning/mlperf-benchmark-establishes-that-google-cloud-offers-the-most-accessible-scale-for-machine-learning-training Deep Learning: How to build deep learning inference through Knative Serverless framework https://opensource.com/article/18/12/deep-learning-inference Batch services: If

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Best Practices for running HPC on AWS

December 11th, 2018
Best Practices for running HPC on AWS

Use placement group A cluster placement group is a logical grouping of instances within a single Availability Zone. Cluster placement groups are recommended for applications that benefit from low network latency, high network throughput, or both, and if the majority of the network traffic is between the instances in the group. Reference: https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/placement-groups.html   Disable

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Cloud weekly news digest (9.12.2018)

December 9th, 2018
Cloud weekly news digest (9.12.2018)

Machine Learning: AWS Inferentia – a machine learning inference chip designed to deliver high performance at low cost: https://aws.amazon.com/about-aws/whats-new/2018/11/announcing-amazon-inferentia-machine-learning-inference-microchip/ https://aws.amazon.com/machine-learning/inferentia/ AWS-Optimized TensorFlow Now Scales to 256 GPUs: https://aws.amazon.com/about-aws/whats-new/2018/11/tensorflow-scalability-to-256-gpus/ New features for AWS SageMaker: https://aws.amazon.com/about-aws/whats-new/2018/11/amazon-sagemaker-announces-support-for-reinforcement-learning/ https://aws.amazon.com/about-aws/whats-new/2018/11/introducing-amazon-sagemaker-groundtruth/ Azure Machine Learning service: A look under the hood https://azure.microsoft.com/en-us/blog/azure-machine-learning-service-a-look-under-the-hood/ ONNX Runtime – a high-performance inference engine for machine learning

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IUCC Featured in “In the Field”

December 5th, 2018
IUCC Featured in “In the Field”

A consortium of seven researchers from four countries in the HORIZON 2020-funded Sweeper project, including Ben Gurion University of the Negev researchers, are hard at work on a fully automatic robot that can sense, identify, classify maturity, and pick sweet peppers in a greenhouse that are ready for market. Efficiently, safely and quickly. Read the

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Cloud weekly news digest (25.11.2018)

November 25th, 2018
Cloud weekly news digest (25.11.2018)

Machine Learning: Automated Machine Learning in Azure and Google cloud: https://medium.com/@santiagof/auto-is-the-new-black-google-automl-microsoft-automated-ml-autokeras-and-auto-sklearn-80d1d3c3005c Translating Between Statistics and Machine Learning: https://insights.sei.cmu.edu/sei_blog/2018/11/translating-between-statistics-and-machine-learning.html News about Amazon SageMaker: https://aws.amazon.com/blogs/machine-learning/new-features-for-amazon-sagemaker-workflows-algorithms-and-accreditation/ https://aws.amazon.com/blogs/machine-learning/amazon-sagemaker-automatic-model-tuning-becomes-more-efficient-with-warm-start-of-hyperparameter-tuning-jobs/ https://aws.amazon.com/blogs/machine-learning/easily-monitor-and-visualize-metrics-while-training-models-on-amazon-sagemaker/ Batch processing: If you need to run tightly coupled and high compute research, Amazon batch service now supports multi-node parallel jobs: https://aws.amazon.com/about-aws/whats-new/2018/11/aws-batch-now-supports-multi-node-parallel-jobs/ https://docs.aws.amazon.com/batch/latest/userguide/multi-node-parallel-jobs.html Data Science: How Data Science Is Improving

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Cloud weekly news digest (18.11.2018)

November 18th, 2018
Cloud weekly news digest (18.11.2018)

HPC: Three Approaches to HPC and AI Convergence: https://openlab.cern/sites/openlab.web.cern.ch/files/2018-09/Three%20Approaches%20to%20HPC%20and%20AI%20Convergence%20-%20IT%20Peer%20Network.pdf Amazon released a new service called ParallelCluster in-order to simplify HPC cluster management: https://aws.amazon.com/blogs/opensource/aws-parallelcluster/ GPU: Case study from the Italian National Institute of Astrophysics who migrated to AWS for astronomy research: https://aws.amazon.com/blogs/publicsector/the-italian-national-institute-of-astrophysics-explores-the-universe-with-the-cloud/ Storage and security related: AWS now offers a policy for securing S3 buckets using

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Cloud weekly news digest (11.11.2018)

November 11th, 2018
Cloud weekly news digest (11.11.2018)

Machine Learning: Latest news about Amazon SageMaker service for researchers: https://aws.amazon.com/blogs/machine-learning/lifecycle-configuration-update-for-amazon-sagemaker-notebook-instances/ https://aws.amazon.com/blogs/machine-learning/customize-your-notebook-volume-size-up-to-16-tb-with-amazon-sagemaker/ https://aws.amazon.com/blogs/machine-learning/now-easily-perform-incremental-learning-on-amazon-sagemaker/ https://aws.amazon.com/blogs/machine-learning/introduction-to-amazon-sagemaker-object2vec/ https://aws.amazon.com/about-aws/whats-new/2018/11/amazon-sagemaker-now-supports-apache-mxnet-1-3-and-tensorflow-1-/ Big Data: The pros and cons of Google Cloud Storage vs. HDFS https://cloud.google.com/blog/products/storage-data-transfer/hdfs-vs-cloud-storage-pros-cons-and-migration-tips Tips and tricks for migrating on-premises Hadoop infrastructure to Azure HDInsight https://azure.microsoft.com/en-us/blog/migrating-on-premises-hadoop-infrastructure-to-azure-hdinsight/ HPC: What is HPC in the Cloud? Exploring the Need for Speed https://blogs.oracle.com/cloud-infrastructure/what-is-hpc-in-the-cloud-exploring-the-need-for-speed Cost optimization:

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