Machine Learning Applications for Business

Artificial Intelligence

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26 Apr 2022
7 Min read

A Guide to Developing Machine Learning Applications for Business

Most businesses currently utilize machine learning (ML) tools to assess revenue possibilities, identify market trends, predict customer behavior and pricing fluctuations, and make the best business decisions. The creation of these machine learning applications needs painstaking planning and procedures. Problem conceptualization, data purification, feature engineering, model training, and improving model accuracy are all approaches for creating machine learning applications.

Machine learning is a subset of AI technology that helps with data interpretation and decision-making. Machine learning is a collection of techniques for finding patterns in data and building mathematical models from them.

We can use that model to forecast future data once we’ve constructed and trained a machine-learning algorithm to generate a mathematical representation of these data. For example, we might apply a learning algorithm to forecast whether a customer will buy a specific product based on previous purchases in the retail industry.

When should you apply machine learning?

Machine learning is a powerful technique, but it should not be utilized frequently because it is computationally costly and regularly requires model training and updating. Using traditional software rather than machine learning is sometimes desirable.

Without applying machine learning, we can develop a robust solution for certain use cases depending on rules, simple computations, or pre-determined processes for results and decision-making. These gadgets are easy to program and don’t require a lot of experience. As a result, experts suggest that machine learning be used in the following scenarios:

ML techniques can be applied in two different scenarios:

The inability to code the rules:

  • Tasks that are impossible to do using a set of rules
  • It’s difficult to find and apply rules
  • Multiple rules must be followed simultaneously, which is difficult to code
  • Coding the rules based on such criteria is difficult due to other concerns
  • Erroneous codes are the outcome of overlapping regulations

The scale of data is high:

When you can make rules based on a few examples, it’s challenging to look over millions of data sets to better predict.

Machine learning can be utilized in both of the situations above since it creates a mathematical model with rules and can deal with large-scale problems.

The steps for creating machine learning apps are as follows:

A machine learning application is created through an iterative process that follows steps. The steps for developing machine learning applications are as follows:

Identifying the problem

This is the first stage in defining a machine learning problem regarding the prediction we want to make and the type of observation data we have. A label or a target answer, which could be a yes/no label (binary classification), a category (multiclass classification), or an actual number, is frequently included in predictions (regression).

Data collection and cleaning are required

The following stage is to collect data from a history database, open datasets, or any other data sources after identifying the problem and deciding what kind of historical data we have for predictive modeling.

Not all of the data collected is relevant to machine learning. Unnecessary data may need to be deleted, lowering prediction accuracy or necessitating additional calculations without improving the conclusion.

Prepare data for machine learning

After preparing the data for the machine learning algorithm, we must change it into a format that the ML system can interpret. An image or word is incomprehensible to machines. We’ll have to turn it into numbers. It also necessitates the creation of a data pipeline based on the machine learning application’s requirements.

Feature engineering

Raw data does not always reveal all of the facts about the label under consideration. Feature engineering combines two or more existing features with a more relevant and intelligible arithmetic operation to create new features.

Developing a model

To determine how effectively the model generalizes to new data, we must divide the data into training and evaluation sets before training it. The algorithm will now learn the pattern as well as the feature-to-label mapping.

Depending on the activation function and method utilized, learning can be linear or non-linear. Learning rate, regularization, batch size, number of passes (epoch), optimization technique, and other factors all impact how long it takes to learn and train.

The accuracy of the model is being assessed and improved

The accuracy of a model is measured by how well it performs on an unknown validation set. We need to see how a model performs on a validation set based on our current knowledge. Depending on the application, several accuracy metrics may be utilized.

When a model fails to perform as expected, the issue can be characterized as 1) over-fitting or 2) under-fitting.

Over-fitting occurs when a model performs well on training data but not on validation data. In any case, the model does not appear to generalize well. Regularizing the data, decreasing input features, eliminating duplicated features, and resampling techniques such as k-fold cross-validation can help solve the problem.

A model is considered under-fitted if it performs badly on both the training and validation datasets. Additional data training, research into different algorithms or structures, more passes, and experimenting with learning rate or optimization techniques are all possibilities.

The technique will develop a model to represent those labels from input data after iterative training, which can be used to forecast unknown data.

Servicing a model in production

After training, the model will perform well on unknown data, and it may now be used for prediction. This is the most important factor for businesses. This is also one of the most difficult business-oriented machine learning systems stages. To generate the findings in this step, we deploy the model in production to predict real-world data.

Conclusion

Machine learning is the enabler technology, but we risk failing unless we follow strict training and learning algorithm-based model design and execution. As a result, companies looking to develop complex machine learning systems should hire AI and Machine Learning service providers and concentrate on their core skills.

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