Now, we clearly understand what machine learning is and what the key tasks to be performed in a learning problem are. The four main activities to be performed for any machine learning problem are as follows:

1. Define the machine learning problem
2. Gather the data required
3. Use that data to build/train a model
4. Use the model to make predictions

Training the model is the most difficult part of the whole process. Once we have trained the model and have the model ready, using it to infer or predict for a new dataset is very easy.

For all the four steps provided in the preceding points, we clearly need to decide where we intend to use them—on a device or in the cloud.

The main things we need to decide are as follows:

• First of all, are we going to train and create a custom model or use a prebuilt model?
• If we want to train our own model, do we do this training on our desktop machine or in the cloud? Is there a possibility to train the model on a mobile device? 
• Once the model is available, are we going to put it in a local device and do the inference on the device or are we going to deploy the model in the cloud and do the inference from there?

The following are the broad possibilities to implement machine learning in mobile applications. We will get into the details of it in the upcoming sections: