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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....

For our production workload, we deploy the workloads on Kubernetes, in which an ingress resource is created per each deployment. Resources in ingress are GCP Load Balancer and SSL Certificate. As for DNS, we use Cloudflare since it enables CDN without extra configurations on our part. A few months after the deployment went live initially, we were informed that the website couldn’t be accessed. Turns out GCP couldn’t renew the SSL Certificate (error FAILED_NOT_VISIBLE....

For a machine learning project, usually it is divided into two main categories: research and production. For research ML project, the model would be created and used locally on a researcher’s machine. For a production ML project, a deployment would be involved. Usual pattern is to create a service to load a model, accept input, then return a prediction. Production ML is also divided into two main patterns: batch or real-time....