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Command-line Tools can be 235x Faster than your Hadoop Cluster (2014)

Hacker News

He writes about ML/AI/crypto/data, leadership, and building tech teams. Adam Drake is an advisor to scale-up tech companies.

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Top 6 Kubernetes use cases

IBM Journey to AI blog

Developed internally at Google and released to the public in 2014, Kubernetes has enabled organizations to move away from traditional IT infrastructure and toward the automation of operational tasks tied to the deployment, scaling and managing of containerized applications (or microservices ).

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Philips accelerates development of AI-enabled healthcare solutions with an MLOps platform built on Amazon SageMaker

AWS Machine Learning Blog

Since 2014, the company has been offering customers its Philips HealthSuite Platform, which orchestrates dozens of AWS services that healthcare and life sciences companies use to improve patient care. In this post, we describe how Philips partnered with AWS to develop AI ToolSuite—a scalable, secure, and compliant ML platform on SageMaker.

ML 127
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Implement smart document search index with Amazon Textract and Amazon OpenSearch

AWS Machine Learning Blog

You need permissions to deploy AWS CloudFormation templates, push to the Amazon Elastic Container Registry (Amazon ECR), create Amazon Identity and Access Management (AWS IAM) roles, Amazon Lambda functions, Amazon S3 buckets, Amazon Step Functions, Amazon OpenSearch cluster, and an Amazon Cognito user pool.

AWS 133
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Effectively solve distributed training convergence issues with Amazon SageMaker Hyperband Automatic Model Tuning

AWS Machine Learning Blog

Amazon SageMaker distributed training jobs enable you with one click (or one API call) to set up a distributed compute cluster, train a model, save the result to Amazon Simple Storage Service (Amazon S3), and shut down the cluster when complete. Finally, launching clusters can introduce operational overhead due to longer starting time.

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Introduction to Kubernetes

Snorkel AI

The project itself debuted in 2014, and has become the infrastructure backbone of many modern software companies and their products. Each k8s cluster is made up of two key components: the k8s control plane and an arbitrary number of attached worker nodes whose sole job is to run containers.

ML 52
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Introduction to Kubernetes

Snorkel AI

The project itself debuted in 2014, and has become the infrastructure backbone of many modern software companies and their products. Each k8s cluster is made up of two key components: the k8s control plane and an arbitrary number of attached worker nodes whose sole job is to run containers.

ML 52