Distributing large pieces of software to thousands of machines with a wide variety of configurations can pose a significant operational challenge, requiring a process to identify and copy precisely the right combination of dependent libraries and data files for each device. To make this faster, more robust, and more efficient, we have developed and deployed XARs, or eXecutable ARchives, a system f
Amazon Web Services ブログ AWS Data Wranglerを使って、簡単にETL処理を実現する 2019年9月、Github上にAWS Data Wrangler(以下、Data Wrangler)が公開されました。Data Wranglerは、各種AWSサービスからデータを取得して、コーディングをサポートしてくれるPythonのモジュールです。 現在、Pythonを用いて、Amazon Athena(以下、Athena)やAmazon Redshift(以下、Redshift)からデータを取得して、ETL処理を行う際、PyAthenaやboto3、Pandasなどを利用して行うことが多いかと思います。その際、本来実施したいETLのコーディングまでに、接続設定を書いたり、各種コーディングが必要でした。Data Wraglerを利用することで、AthenaやAmazo
Feature selection, the process of finding and selecting the most useful features in a dataset, is a crucial step of the machine learning pipeline. Unnecessary features decrease training speed, decrease model interpretability, and, most importantly, decrease generalization performance on the test set. Frustrated by the ad-hoc feature selection methods I found myself applying over and over again for
Focus on parameters PlanOut provides randomized parameter values to Internet services. Instead of using constants for user interface elements or switches controlling the rollout of a new feature or ranking model, just use PlanOut to determine the value of these parameters. Now you have an experiment. Simple or complex It is easy to implement simple A/B tests in PlanOut. But it's also not much hard
Graphite does three things: Kick ass. Chew bubblegum. Make it easy to store and graph metrics. (And it’s all out of bubblegum.) Learn more about Graphite About the Project Graphite is an enterprise-ready monitoring tool that runs equally well on cheap hardware or Cloud infrastructure. Teams use Graphite to track the performance of their websites, applications, business services, and networked serv
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