Monitoring and Tuning Machine Learning Programs Based On Your Own Needs


Monitoring and Tuning Machine Learning Programs Based On Your Own Needs Team
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It can sometimes be difficult to figure out whether your machine learning program is performing as it should. That is why you need to have your own defined metrics to check to ensure that you are getting the results you want from your program. For example, the results an e-commerce website with machine learning will get are different from a music streaming service. An e-commerce website would want to get as many sales as possible, and that would mean they would have to focus on monitoring metrics that gave them the highest average selling price possible for each customer. The streaming service wants to keep you as a subscriber for as long as possible, so they must have personalized songs that you will want to play every day.

Judging When a Machine Learning Program Isn’t Working As Expected

You must set your own custom performance metrics to monitor to know when your machine learning program isn’t working as it should. Those metrics should start with what your users are expecting. For example, if you users expect a personalized homepage, you can easily check how many users are getting a personalized homepage when they visit. If you see that many people are not getting the homepage they expect, it can be easy to tell that something is wrong. You can even dig deeper into those metrics. For example, you can determine that if a user doesn’t have five personalized songs, then they are not getting the homepage they expect. It would also mean that there is something wrong with the program. Tuning and monitoring your machine learning program to those situations is crucial for your success in this arena. You also have to determine what will constitute a bad response for your users. In the case of an audio system, a bad user experience would be users having random songs on their front page.

Setting Up Alerts to See When Something Goes Wrong

In machine learning, it is not enough for you to measure these metrics; you also have to build alerts into your program to ensure that you always know when something is going wrong. You also need a system to evaluate whether your machine learning program is working properly without touching the live program. You can think of this as an area for you to back-test everything that needs to work before your program can be deployed into a live service. It is crucial to understand these systems because you will be the person who evaluates what the end result looks like.

Noise Control

A big problem that many machine learning models face is dealing with bot traffic. In fact, it is going to be one of the most difficult things for you to figure out. The key thing to remember is that bot traffic will behave differently, and it might change your machine learning metrics. It can introduce noise that pollutes your models and causes problems in the future. Your dashboard needs to have alerts built-in for these types of events. They should tell you what it thinks a bot is skewing the results, and it should allow you to monitor things successfully.

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Enterprise AI/ML Application Lifecycle Management Platform

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