Lambda functions are a great way to put small workloads into the cloud without needing to care about servers or scalability. They work well with Python and the data format you’ll most likely be using to exchange data is JSON. I help you listen through the noise in machine learning: This is a very brief post to show how to do this in Python, in particular how to pass JSON data to the lambda function and how to read the JSON result.
PyTorch is great to quickly prototype your ideas and get up and running with deep learning. Since it is very pythonic, you can simply debug it in PyCharm as you are used to in regular Python. However, when it comes to serving your model in production the question arises: how to do it? There are many possibilities to do so, but in this post, you will learn how to serve it as a lambda function in a serverless manner on AWS.
So, we all regularly create backups of important data, right? Right. Well, at least I hope you do. For your personal data as well as any company data you are dealing with. It is fairly easy and straightforward to create e.g. a MySQL backup on a server, zip it up and always keep the last 10 days as copies. However, what happens in case your whole server’s disks have an unrecoverable crash?