To make a successful decision on business development, it is necessary to consider several options for the development of events. The customer does not always need to delve 100% into the technical details. But still, if you work with big data or artificial intelligence, it will not be superfluous to delve a little into their architecture. The best way to do this is to use Data Science consulting services. Experienced specialists will explain the most basic points and help organize information.
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Implementation options for machine learning patterns
In most occasions, the actual utility of machine learning techniques is a central part of development, even if it is a little constituent element of an email automation system or chatbot. Sometimes the obstruction to realization appears insurmountable.
Most ML specialists apply R or Python for scientific development. However, the consumers of these techniques will be Developers who use a very different technology stack. There are 2 options for solving this issue:
- Rewrite all the code in the language that development engineers apply in their projects. It sounds logical to some extent, but it takes a lot of effort and time to replicate the developed techniques. In the end, it’s just a waste of time. Most languages for front-end development don’t have a convenient library for working with ML. Therefore, it would be quite a rational decision not to use this option.
- Use the Application Programming Interface (API). Network APIs solved the problem of working with applications in different languages. If a front-end coder has to apply your machine learning technique to create and launch an app from it, he can simply get the URL of the backend server that is serving as API and that’s all.
Findings
Before making a final decision, it is very important to consult with specialists. Every business is unique. A customer should be based on his or her goals and capabilities. If you decide to use the API consider the next points:
- Building a quality API from spaghetti code is almost impracticable. Use your knowledge of ML to create a useful and usable API.
- Attempt applying release monitoring for models and API. Saving and keeping track of ML techniques is a tricky task, find a way that works for you. Or leave it to the development department.
- Check that the evaluator and training code are close by. Thus, the mothballed pattern will have a class evaluator next to it.
The following phase is to build a mechanic for deploying such an API to a small virtual machine.
