Basics of K-Means Clustering

Machine Learning is considered as the execution of utilizing the existing algorithms, in order to inject data, grasp from it, and then make a resolution or forecast about something. So rather than developing software procedures with a certain set of directives to achieve a specific task, the machine is instructed using huge amounts of data and algorithms that provides it the capability to absorb, how to accomplish the endeavour.

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Cloud Enabled DevOps Strategy on AWS

Any organization which is serious about releasing iterations of bug free software, in a frequent manner should have some level of DevOps processes in place in their delivery pipeline. The following post will discuss how to implement a DevOps continuous delivery/deployment pipeline in the AWS Cloud infrastructure.

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Machine Learning with AWS

Machine learning often feels a lot harder than it should be to most developers because the process to build and train models, and then deploy them into production is too complicated and too slow. First, you need to collect and prepare your training data to discover which elements of your data set are important. Then, you need to select which algorithm and framework you’ll use.

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AWS Kinesis Firehose – Real-time data streaming on AWS

AWS Kinesis Firehose is a fully managed service for transforming and delivering streaming data to a given destination. A Destination can be a S3 bucket, Redshift cluster, Splunk or Elasticsearch Service. In the following tutorial I’ll walk you through the process of streaming CloudWatch Logs to a S3 bucket generated by an AWS Lambda function.

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Building SaaS based Enterprise Cloud Applications – White Paper

According to NIST, “cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction (NIST, 2011The traditional approach incur a huge capital expenditure upfront along with too much excess capacity not allowing to predict the capacity based on the market demand.

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Setting up a testing cluster using ClusterRunner

So you have written some tests for your project and now you are waiting for the test run to be completed to see if anything breaks due to the changes you have made by your last commit. Finally you can merge your changes to develop when everything seems green on your CI. But when the number of tests increases, their execution time will also increase.

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