A minimal application architecture would compose of a database, and an application backend. Serverless database is still in its infancy, but thankfully container-based runtime is very much alive and doing well. On GCP, a serverless container-based runtime do exist, known as Cloud Run. Standard database access pattern Per standard security practices, you should not expose your database to public, this means you should use a proxy/tunnel or private network to reach your database. ...

Back in 2017-2018, everyone wanted to be a data scientist. Then reality hits, that they need a data engineer for a successful machine learning project. Things didn’t end there, since they also need a machine learning engineer to create production-ready code. Some people think you only need an MLE and suddenly your ML project would become a reality, sadly the reality begs to differ, because you also need to find someone to deploy and scale it, enter DevOps engineer (who understands ML, this is very important). ...

Many data science teams are struggling with implementing end-to-end machine learning projects. It’s a very common phenomenon, so if you are experiencing this, you are not alone. Having worked in every stage of data science project lifecycle, in addition to normal web services deployments, this is what I think how we should collaborate. Collaboration model between teams Note: The diagram does not signify order of communication. Rather, it states the communication pathways between teams. ...

Over the years I’ve heard stories of data teams struggling with deploying machine learning models to production. Clearly there is a pattern here. This article is my reflection on the matter. So what’s the problem? Data scientists, by definition, create mathematical models from data so some unknowns can become known. This is colloquially known as “prediction.” For example, if you have sales data from last year, you can use it to forecast sales performance of next year. ...

There are multiple reasons why someone wants to set up a postgres locally. Either for learning SQL or as an application’s backend. Over the years I see people struggle with using postgres locally, so here are common use cases and possible issues, with solutions for each. For Learning SQL SQL is very common for analysts to use for accessing data from a database, because the data size outgrows Excel. However, SQL is a query language, not a database engine. This essentially means if you want to get familiar with SQL, there are other simpler alternatives, such as SQLite or DuckDB (which can load data from local files directly without doing an explicit data import). Plus, you don’t need authentication to use either of them! ...