Скачать или смотреть Beginners Guide To AWS SageMaker - Create your first ML Model

In this video we take a look at AWS SageMaker and it's machine learning capabilities. I guide you through an AWS tutorial where we build an ML model using the XGBoost algorithm. This estimates the likelihood that a bank customer will take out a Credit Deposit with the Bank.

00:00 - Intro
00:22 - What Is AWS SageMaker?
00:48 - When Do We Use SageMaker?
01:12 - Why Use AWS SageMaker?
01:47 - What The Tutorial Will Cover
02:25 - Hands On SageMaker Tutorial

Git Hub
https://github.com/johnny-chivers/sag...

Website
https://johnnychivers.co.uk

Discourse Forum
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AWS - Step by Step
https://aws.amazon.com/getting-starte...

What AWS Say
Amazon SageMaker is a fully managed machine learning service. With SageMaker, data scientists and developers can quickly and easily build and train machine learning models, and then directly deploy them into a production-ready hosted environment. It provides an integrated Jupyter authoring notebook instance for easy access to your data sources for exploration and analysis, so you don't have to manage servers. It also provides common machine learning algorithms that are optimized to run efficiently against extremely large data in a distributed environment. With native support for bring-your-own-algorithms and frameworks, SageMaker offers flexible distributed training options that adjust to your specific workflows. Deploy a model into a secure and scalable environment by launching it with a few clicks from SageMaker Studio or the SageMaker console. Training and hosting are billed by minutes of usage, with no minimum fees and no upfront commitments.