You might be familiar with **Amazon Web Services(AWS), **one of the most popular cloud services from Amazon, which provides cloud computing infrastructure in the form of various modular and scalable building blocks. Things like Computing Instances, Storage, Database, Network Transport Layers, Security, and Traffic Monitoring are building blocks at your disposal that could be used to create and deploy any type of application in the cloud. The scalable nature of the AWS cloud instances makes it easy to create applications and services in a live production setting. Machine Learning and Artificial Intelligence applications are the natural progression for us to make good use of AWS. Luckily, Amazon addresses this use case for us and AWS SageMaker is configured with all AWS resources so that it streamlines the build, train, test, and deploy process for ML & AI.

This post will give you a gentle introduction to AWS SageMaker on the following sections without the need for prior AWS training/certification.

1. Introduction to SageMaker & Issues to solve

2. Features of AWS SageMaker

3. Case Study: Building a Movie Recommendation Service in a live production setting

4. Pros and Cons of AWS SageMaker

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So what is the use case for Amazon SageMaker and what issues can it help us to address?

As you probably know, if you are doing machine learning today, the workflow involves complex and extraneous steps all the way from data preparation, data cleaning, feature engineering, choosing and building the model, setting up the learning environment, training, tuning and debugging the model, managing versions of the models, deploying the model, monitoring the performance of the model, validating the results and eventually scaling for the production environment. Developers (Software Engineers and Data Scientists) nowadays use a collection of different tools to achieve this complex workflow for machine learning systems, and it is not so easy to have a seamless development experience from experimentation to live production.

#machine-learning #aws #cmu #ai #amazon-sagemaker