CLOUD LABS
Deploying a Machine Learning Model with Amazon SageMaker
In this Cloud Lab, you’ll learn how to deploy a machine learning model with Amazon SageMaker, provide access to it with a Lambda function, and trigger the Lambda function with API Gateway.
beginner
Certificate of Completion
Learning Objectives
Amazon SageMaker is an AWS-managed service that provides machine learning services. It provides an integrated Jupyter Notebook. Data scientists can easily access data, analyze and extract its features, train a machine learning model, evaluate it, and deploy it on a hosted environment. It provides native support for making our own machine learning algorithms and some commonly used algorithms that perform well on huge datasets.
In this Cloud Lab, you’ll make a notebook instance in Amazon SageMaker, deploy a machine learning model on the notebook instance, and host the model on an endpoint. Moreover, you’ll use a Lambda function to access the endpoint. At the end of the Cloud Lab, you’ll use an API gateway to trigger the Lambda function with a payload and get predictions in response.
The following is the high-level architecture diagram of the infrastructure you’ll create in this Cloud Lab:
Relevant Courses
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Your method is simple, straight to the point and I can practice with it everywhere, even from my phone, that's something I have never had in other learning platforms.
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I highly recommend Educative. The courses are well organized and easy to understand.
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I prefer Educative courses because they have a nice mix of text & images. I find that with full video courses, it can often be too easy to go into passive learning mode.
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