Machine Learning Deployments

Lets start with some of the basics. What is the simplest form of machine learning ? If you consider machine learning as utilizing previous data to predict future outcomes then taking average is one of the simplest ways. For example, if you predict the todays temprature from the historical temprature data of same day you will get a reasonable estimate. Don't underestimate this the theories of law of large numbers and central limit is very powerful especially if you don't have additional variables to explain the data. However, often we have explonotary variables. In machine learning nomeclature we call them features. In this setting, each feature lies in euclidean vector space and a function maps these features to target variable. Now the task is finding that function. Of course, you can just construct lookup table for input output pairs from previous data. If you don't have much variation from cases in runtime vs previous data this will do the job. But the real problem of ...

Machine learning has changed numerous applications and would continue to change with more availability of data. Once you look at the literature, you can find many different documents describing algorithms. However, one aspect that I feel needs a more comprehensive overview is the deployment of those algorithms. In this blog channel, I would like to go over alternatives for the deployment methods. Let me name a few: REST API backends Desktop applications Edge devices Mobile deployment Script Executables Web front ends No deployment There are many pros and cons for each method in terms of latency and resource usage. We will be discussing them in more detail.