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Note: You can this tutorial as a video on youtube.

How does a machine learning engineer make their model results accessible to end-users via the web? This once-daunting task is now pretty straightforward using a combination of AWS services, especially SageMaker and Lambda.

Amazon SageMaker is a powerful tool for machine learning: it provides an impressive stable of built-in algorithms, a user interface powered by jupyter notebooks, and the flexibility of rapidly training and deploying ML models on a massive range of AWS EC2 compute instances. But even the most accurate model provides no benefit if it’s inaccessible to other people! Our end-users can’t log into our SageMaker instance and run our notebooks, so it’s important to connect our finished model to a web-facing application. …

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