Technology is moving at a rapid pace, and quite naturally Machine Learning is emerging as one of the most sought-after technologies in this era. Quite naturally, building ML apps has turned out to be a necessity for businesses today. Are you trying to figure out how to build machine learning applications? Have you been looking for a simple guide that can walk you through the process? At Top Flight Apps, the professionals know the steps to ML app development. Their blog has an excellent section about how to build a machine learning app, from where you can understand the process in detail. But this blog will tell you a little about the steps in a precise manner.
The process of developing a machine learning application involves a particular set of steps. It is an iterative process requiring you to follow the sequence of steps. The sequence of steps are as follows:
The first step to create a machine learning app from scratch is to frame a problem. It will be in terms of the predictions you want to make and the observation data that will be used. Predictions can be a binary classification, a real number or a multiclass classification.
The next step for machine learning app development revolves around collecting data for prediction modelling. The data can be collected from open sources, historical databases or any other sources. But all the acquired data might not be relevant for developing the app. It calls for removing irrelevant data that can negatively impact the accuracy of predictions.
After the data has been filtered, it needs to be converted in a form that’s understandable by the machine learning system. Any data in the form of text or images have to be converted into numbers. Moreover, data pipelines also need to be built on the basis of machine learning requirements. But sometimes, raw data won’t be able to reveal everything about a targeted label. Feature engineering has to be used to develop additional features by combining a few existing ones with arithmetic operations.
To build a machine learning app, it is essential to train a model. But before that, data needs to be categorised into training and evaluation sets. It allows monitoring to what extent a model can generalise unseen data. As a result, the algorithm can analyse the mapping and pattern between the label and the feature.
Accuracy depends on how well a model is performing on an unseen validation set. The metrics to measure accuracy will depend on the application. Sometimes a model might perform well on training data but not so good on validation data. It results in an over-fitting scenario that can be solved with the help of resampling techniques such as k-fold cross-validation. Reducing input features, removing redundant features and regularising the algorithm can also help.
When the model does not perform well on both the training and validation set, it is an under-fitting scenario. It can be solved by using more data sets for training and increasing the number of passes. Evaluating the architectures and algorithms also becomes necessary in case of an under-fitting scenario. Sometimes experimenting with the optimisation algorithm and learning rate can also solve this issue.
The last step to build a machine learning mobile application is the most difficult one for machine learning applications to be used for business. Once the training is over, the model will perform well on unseen data and become capable of making predictions. Therefore, the model will be deployed to make predictions on real-world data. The results derived will help in determining if the app could have been built successfully.
Hopefully, you will no longer frantically search the internet to know how to build machine learning applications. After you successfully create the machine learning application, the decision-making process in your business will improve. The use of artificial intelligence by these apps will also help derive sense out of historical data.