On December 30, 2018, IUCC presented at a conference for researchers who use, or are interested in using, public cloud services. The conference took place in Kfar Hamacabiah. Around 20 researchers from different universities attended. The conference was part of a developers’ conference hosted and organized by the Sela Group, for the benefit of IUCC
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
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
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,
Cloud Workshop (in Hebrew) designed especially for researchers will take place on December 30, 2018 at Kfar Hamacabia. More information.
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
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
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
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
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
