4 steps in building effective machine learning models

Building machine learning models that have the ability to generalize well on future data requires thoughtful consideration of the data at hand and of assumptions about various available training algorithms. Ultimate evaluation of a machine learning model’s quality requires an appropriate selection and interpretation of assessment criteria.

Machine learning consists of algorithms that can automate analytical model building. Using algorithms that iteratively learn from data, machine learning models facilitate computers to find hidden insights from Big Data without being explicitly programmed where to look. This has given rise to a plethora of applications based on Machine learning.

As machine learning is in such a demand now, we will discuss how organizations can systematically build machine learning models: 

1.   Keep your machine learning model simple

While it is fun to think about all the tasks machine learning can do, it will be hard to figure out what is happening if your machine learning model is complicated. You must keep the first iteration of your model simple and focus on getting the infrastructure right. The first model provides the biggest boost to your product that is why it doesn't need to be fancy. But you will run into many more infrastructure issues than you expect. Before you expect to gain value from your machine learning models, you should determine:

• Examples for your learning algorithm
• What “good” and “bad” mean to your system
• How to integrate your model into your application?

Choosing simple features will make it easier for you to ensure that:

• The features reach your machine learning algorithm correctly
• The features reach your model in the server correctly
• The machine learning model learns reasonable weights

Once you have a system that does these three things correctly, it will provide you with baseline machine learning metrics and a baseline behavior that you can use to test more complex models.

2.   Detect problems before exporting machine learning models

Several machine learning systems have a stage where you export the model to serving. If there is a problem with an exported model, it is a user facing an issue. If there is a problem before, then it is a training issue and users will not notice. Thus, to avoid these problems conduct sanity checks right before you export the model. Make sure that the model’s performance is reasonable on held out data. Or, if you are having lingering concerns with the data, don’t export a model.

3.   Design metrics

Choose a simple and attributable metric for your machine learning. Several companies do not know what their true objective is. Your machine learning objective should be something that is easy to measure. So start by training on the simple machine learning objectives and consider having a "policy layer" on top the first layer that will allow you to add additional logic. The easiest objective to model is a user behavior that can be directly observed: Was this ranked link clicked by users?

• Was this ranked object downloaded?
• Was this ranked object shared or forwarded?
• Was this ranked object rated?

You must avoid modeling indirect effects at first:

• Did the user visit the next day?
• How long did the user visit the site?
• What were the daily active users?

4.   Plan to launch and iterate

You should not expect that the model you are working on now will be the last one that you will launch. You should first consider whether the complexity you are adding in the first model is necessary. Many teams have launched machine learning models per quarter or more, for years. There are three basic reasons for launching new models:

• You are developing new features
• You are tuning and combining old features in new ways
• You are tuning objectives.

For gaining value from your machine learning models, you have to know how to pair best algorithms with the right tools and processes. This will ensure that your models run as fast as possible, in huge enterprise environments.