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Introducing Multimodal Clustering

DataRobot

Yes, data created over the next three years will far exceed the amount created over the past 30 years ( Source : IDC Worldwide Global DataSphere Forecast, 2020-2024). Clustering is a technique that can be used to get a sense of the data while allowing to tell a powerful story. Introducing Multimodal Clustering. Name Clusters.

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Deploy Amazon SageMaker pipelines using AWS Controllers for Kubernetes

AWS Machine Learning Blog

ACK allows you to take advantage of managed model building pipelines without needing to define resources outside of the Kubernetes cluster. Prerequisites To follow along, you should have the following prerequisites: An EKS cluster where the ML pipeline will be created. kubectl for working with Kubernetes clusters.

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Big Data Skill sets that Software Developers will Need in 2020

Smart Data Collective

Software businesses are using Hadoop clusters on a more regular basis now. The post Big Data Skill sets that Software Developers will Need in 2020 appeared first on SmartData Collective. They’re looking to hire experienced data analysts, data scientists and data engineers.

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Satellite Data, Bushfires and AI: Safeguarding Wine Industry Amidst Climate Challenges

Towards AI

Detecting drought in January 2020 (on the left) using the EVI vegetation index Yellow means very healthy vegetation while dark green means unhealthy. Clustering similar fields using unsupervised K-means clustering The outcome of K-means clustering is cluster labels that assign each data point to one of the K clusters.

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“AntMan: Dynamic Scaling on GPU Clusters for Deep Learning” paper summary

Mlearning.ai

Authors of AntMan [1] propose a deep learning infrastructure, which is a co-design of cluster schedulers (e.g., Their motivation for this work was their observation on very low GPU utilization on Alibaba cluster. On the other hands, the second kind is for getting more out of the clusters. Kubernetes, SLURM, LSF etc.)

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Amazon SageMaker model parallel library now accelerates PyTorch FSDP workloads by up to 20%

AWS Machine Learning Blog

As a result, machine learning practitioners must spend weeks of preparation to scale their LLM workloads to large clusters of GPUs. Aligning SMP with open source PyTorch Since its launch in 2020, SMP has enabled high-performance, large-scale training on SageMaker compute instances. To mitigate this problem, SMP v2.0

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Spatial and temporal partitioning of weather data with IBM Cloud Analytics Engine

IBM Data Science in Practice

Temperature observation at 1pm UTC on June 15, 2020 Wind speed observation at 1pm UTC on June 15, 2020 Data usage Most of our clients use weather data as a variable in their linear regression model and other machine learning models. June 2020 is ~540 GB). Please, note the projection issue was left out for simplicity.

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