Google Cloud

Weather forecast is a complicated process. If you live in an area with lots of oscillation in weather like us in Melbourne, you should always give some chance for the weather to be different from what you see on websites. The weather is typically forecasted by first gathering a lot of information about the atmosphere, humidity, wind, etc. and then relying on our atmospheric knowledge and a physical model to articulate changes in the near future. But due to our limited understanding of the physical model and the chaotic nature of the atmosphere, it might be unreliable. Instead of the common approach for this, here we try to scrutinise the idea of entrusting a machine learning model for this purpose. We expect the model to look at the historical data and get a feeling of how the temperature will change in near future, let's say tomorrow.

What is Blobstore? What is a Blob?

Like horse-drawn carriages, video rental stores, and scurvy, Blobstore is a leftover from an earlier time. It is a storage option on Google Cloud Platform (GCP) that stores objects called blobs and associates each blob with a key. It is used with Google App Engine services and allows applications to serve or get files based on an HTTP connection.

Blobstore is now superseded by Google Cloud Storage (GCS) but its usage is still possible with the actual storage in GCS, the same upload behaviour and minimal changes to the app. In contrast to other modules in GCP, migration of Blobstore from one project to another is not straightforward. In this blog, we will investigate this migration.
Recently, I was hunting around the internet, looking for an easy way to extract an attribute of GCP resource to cross-reference while creating another resource in gcloud. I had reserved a static IP address and I wanted to use it's IP address as the external address of a VM instance. I learnt that such a simple operation was indeed tricky, at least up until some time ago. Here's my journey and welcome aboard!