Archive for the ‘Cloud’ Category

Cloud weekly news digest (17.2.2019)

February 17th, 2019
Cloud weekly news digest (17.2.2019)

Machine Learning: Using TensorFlow eager execution with Amazon SageMaker script mode https://aws.amazon.com/blogs/machine-learning/using-tensorflow-eager-execution-with-amazon-sagemaker-script-mode/ Creating hierarchical label taxonomies using Amazon SageMaker Ground Truth https://aws.amazon.com/blogs/machine-learning/creating-hierarchical-label-taxonomies-using-amazon-sagemaker-ground-truth/ AWS – Machine Learning Foundations – Evolution of Machine Learning and Artificial Intelligence https://d1.awsstatic.com/whitepapers/machine-learning-foundations.pdf Making Machine Learning Datasets Unbiased https://www.dataversity.net/making-machine-learning-datasets-unbiased/ Language Models are Unsupervised Multitask Learners https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf Deep Learning and AI: Scheduling GPUs

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Cloud weekly news digest (10.2.2019)

February 10th, 2019
Cloud weekly news digest (10.2.2019)

Machine Learning: Annotate data for less with Amazon SageMaker Ground Truth and automated data labeling https://aws.amazon.com/blogs/machine-learning/annotate-data-for-less-with-amazon-sagemaker-ground-truth-and-automated-data-labeling/ AWS Open Sources Neo-AI for Deploying Machine Learning Models https://awsinsider.net/articles/2019/02/06/aws-neo-ai.aspx Step-By-Step: Getting Started with Azure Machine Learning https://techcommunity.microsoft.com/t5/ITOps-Talk-Blog/Step-By-Step-Getting-Started-with-Azure-Machine-Learning/ba-p/331327 Deep Learning: Deep learning that’s easy to implement and easy to scale https://www.oreilly.com/ideas/deep-learning-thats-easy-to-implement-and-easy-to-scale Data Analytics: Individually great, collectively unmatched: Announcing updates

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Cloud weekly news digest (3.2.2019)

February 3rd, 2019
Cloud weekly news digest (3.2.2019)

Machine Learning: Running Machine Learning experiments on inventory management https://aka.ms/TechCommunity/MicrosoftIgniteTour/DAT40 Operationalizing your new inventory management Machine Learning Model https://aka.ms/TechCommunity/MicrosoftIgniteTour/DAT50 Azure Notebooks http://www.sqlservercentral.com/blogs/martin_catherall/2019/01/31/azure-notebooks-a-nice-little-tool/ Deploy trained Keras or TensorFlow models using Amazon SageMaker https://aws.amazon.com/blogs/machine-learning/deploy-trained-keras-or-tensorflow-models-using-amazon-sagemaker/ Amazon SageMaker Ground Truth – Build Highly Accurate Datasets and Reduce Labeling Costs by up to 70% https://aws.amazon.com/blogs/aws/amazon-sagemaker-ground-truth-build-highly-accurate-datasets-and-reduce-labeling-costs-by-up-to-70/ Disaster Recovery: Configuring Azure Site

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Cloud weekly news digest (27.1.2019)

January 27th, 2019
Cloud weekly news digest (27.1.2019)

Machine Learning: AWS launches open source Neo-AI project to accelerate ML deployments on edge devices: https://aws.amazon.com/blogs/machine-learning/aws-launches-open-source-neo-ai-project-to-accelerate-ml-deployments-on-edge-devices/ Deploy TensorFlow models with Amazon Elastic Inference using a flexible new Python API available in EI-enabled TensorFlow 1.12: https://aws.amazon.com/blogs/machine-learning/deploy-tensorflow-models-with-amazon-elastic-inference-using-a-flexible-new-python-api-available-in-ei-enabled-tensorflow-1-12/ Handling electronic health records: https://aws.amazon.com/blogs/machine-learning/identifying-and-working-with-sensitive-healthcare-data-with-amazon-comprehend-medical/ https://aws.amazon.com/blogs/machine-learning/extract-and-visualize-clinical-entities-using-amazon-comprehend-medical/ https://ai.googleblog.com/2019/01/expanding-application-of-deep-learning.html Introducing Feast: an open source feature store for machine learning based on Google

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Cloud weekly news digest (20.1.2019)

January 20th, 2019
Cloud weekly news digest (20.1.2019)

Machine Learning: Running TensorFlow inference workloads at scale with TensorRT 5 and NVIDIA T4 GPUs: https://cloud.google.com/blog/products/ai-machine-learning/running-tensorflow-inference-workloads-at-scale-with-tensorrt-5-and-nvidia-t4-gpus Getting started with Cloud TPUs: An overview of online resources https://cloud.google.com/blog/products/ai-machine-learning/getting-started-with-cloud-tpus-an-overview-of-online-resources Understand TensorFlow by mimicking its API from scratch: https://medium.com/@d3lm/understand-tensorflow-by-mimicking-its-api-from-scratch-faa55787170d Getting started with TensorFlow Probability from R: https://blogs.rstudio.com/tensorflow/posts/2019-01-08-getting-started-with-tf-probability/ AI: Artificial Intelligence Needs a Strong Data Foundation: https://thefinancialbrand.com/67039/ai-hierarchy-of-data-needs/ Data Analytics:

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Cloud weekly news digest (13.1.2019)

January 13th, 2019
Cloud weekly news digest (13.1.2019)

Machine Learning: Automated and continuous deployment of Amazon SageMaker models with AWS Step Functions: https://aws.amazon.com/blogs/machine-learning/automated-and-continuous-deployment-of-amazon-sagemaker-models-with-aws-step-functions/ What is Azure Machine Learning: https://cloudacademy.com/blog/what-is-azure-machine-learning/ Multi-modal topic inferencing from videos using Azure machine learning services: https://azure.microsoft.com/en-us/blog/multi-modal-topic-inferencing-from-videos/ Curing diseases and delivering effective treatments using Azure machine learning services: https://cloudblogs.microsoft.com/2019/01/08/curing-diseases-and-delivering-effective-treatments-with-the-cloud/ How to automate machine learning on SQL Server 2019 big data

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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 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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