The most difficult part of any machine learning project is putting that machine learning model into production. Without the ability to deploy a model, you have essentially failed. The entire point of machine learning is putting your model into a production environment where it will be used effectively. However, a big question mark is how to deploy machine learning in a way that is effective and quick.
You don’t want to spend time and effort deploying your machine learning model in a way that isn’t conducive to your success. Because of that, there are a few ways to deploy machine learning model technologies that can be decisive in your success.
Build with Containers Connected to an API
Containerization has been a massive trend in software engineering. It is now a major trend in ML deployment. One of the easiest ways to build your app is to use containers. It is quite robust, but it suffers the downside of being very complicated to manage. You essentially connect each container to an API, making it possible to communicate with the other containers in your app. Your machine learning project is then easy to configure and operate on the back end and front end.
That makes it possible to scale quickly, meaning your machine learning model will be able to work on much more data. However, the main downside of being able to deploy machine learning model technologies like this is how long it takes to develop the API. API development is quite challenging, and you might not be able to deploy machine learning for a long time. You would spend a lot of time focusing on your infrastructure without any results.
Python File That Gets Triggered
Another easy way to deploy your model is to use Python and SQL together. You essentially connect your SQL to a batch file that is run every time new data is added. The Python code can then train or retrain the model as needed. You can then use Python code to transmit that updated data to your web application. However, it is quite difficult to work with because you need to have a good environment at all times.
You also have security concerns because you need to put your SQL login information inside the Python file. If your server were to get hacked, everything inside your database would be leaked. That is an obvious problem that makes the solution less than optimal for environments where security is a major concern when building a machine learning project.
Embed Python In the SQL Itself
The final thing you can do is to embed the Python inside SQL itself. The SQL code can run an embedded Python script that trains or retrain the model as needed. You don’t have to deal with containers or APIs, but it can be quite challenging. You’ll be able to do certain things you could not do before, but it offers plenty of security downsides. The major one is the fact that remote code execution will need to be enabled.
You also don’t have access to certain things because the Python environment is only going to be basic. Because of that, you will have to use machine learning project services to build your machine learning model that is built into SQL Server. This can be a problem going forward. However, the variety gives you many options in choosing the best way to build out your project.