Machine Learning for Mobile: Practical guide to building intelligent mobile applications powered by machine learning

Is machine learning applicable to all scenarios? When exactly should wehave the machine learn rather than directly programming the machine with instructions to carry out the task?

Machine learning systems are not knowledge-based systems. In knowledge-based systems, we can directly use the knowledge to codify all possible rules to infer a solution. We go for machine learning when such codification of instructions is not straightforward. Machine learning programs will be more applicable in the following scenarios:

As defined by Tom Mitchell, the problem must be a well-defined machine learning problem. The three important questions to be solved at this stage include the following:

The problem should be such that the outcome that is going to be obtained as a solution to the problem is valuable for the business. There should be sufficient historical data that should be available for learning/training purposes. The objective should be measurable and we should know how much of the objective has been achieved at any point in time.

For example, if we are going to identify fraudulent transactions from a set of online transactions, then determining such fraudulent transactions is definitely valuable for the business. We need to have a sufficient set of online transactions. We should have a sufficient set of transactions that belong to various fraudulent categories. We should also have a mechanism to determine whether the outcome predicted as a fraudulent or nonfraudulent transaction can be verified and validated for the accuracy of prediction.

The data preparation activity is key to the success of the learning solution. The data is the key entity required for machine learning and it must be prepared properly to ensure the proper end results and objectives are obtained.

The following actions need to be performed in order to prepare the data:

A trained model is not exposed to the test dataset during training and any predictions made on that dataset are designed to be indicative of the performance of the model in general. As such, we need to make sure the selection of datasets is representative of the problem that we are solving.

The model-building phase consists of many substeps, as indicated earlier, such as the selection of an appropriate machine learning algorithm, training the model, testing it, evaluating the model to determine whether the objectives have been achieved, and, if not, entering into the retraining phase by either selecting the same algorithm with different datasets or selecting an entirely new algorithm till the objectives are reached.

Once the model is ready, it can be deployed to the field for usage. Predictions can be done on the upcoming dataset using the model that has been built and deployed in the field.

Supervised learning is a type of learning where the model is fed with enough information and knowledge and closely supervised to learn, so that, based on the learning it has done, it can predict the outcome for a new dataset.

Here, the model is trained in supervision mode, similar to supervision by teachers, where we feed the model with enough training data containing the input/predictors and train it and show the correct answers or output. So, based on this, it learns and will become capable of predicting the output for unseen data that may come in the future.

A classic example of this would be the standard Iris dataset. The Iris dataset consists of three species of iris and for each species, the sepal length, sepal width, petal length, and petal width is given. And for a specific pattern of the four parameters, the label is provided as to what species such a set should belong to. With this learning in place, the model will be able to predict the label—in this case, the iris species, based on the feature set—in this case, the four parameters.

Supervised learning algorithms try to model relationships and dependencies between the target prediction output and the input features such that we can predict the output values for new data based on those relationships which it learned from the previous datasets.

The following diagram will give you an idea of what supervised learning is. The data with labels is given as input to build the model through supervised learning algorithms. This is the training phase. Then the model is used to predict the class label for any input data without the label. This is the testing phase:

Again, in supervised learning algorithms, the predicted output could be a discrete/categorical value or it could be a continuous value based on the type of scenario considered and the dataset taken into consideration. If the output predicted is a discrete/categorical value, such algorithms fall under the classification algorithms, and if the output predicted is a continuous value, such algorithms fall under the regression algorithms.

If there is a set of emails and you want to learn from them and be able to tell which emails belong to the spam category and which emails belong to the non-spam category, then the algorithm to be used for this purpose will be a supervised learning algorithm belonging to the classification type. Here, you need to feed the model with a set of emails and feed enough knowledge to the model about the attributes, based on which it would segregate the email to either the spam category or the non-spam category. So the predicted output would be a categorical value, that is, spam or non-spam.

Let's take the use case where based on a given set of parameters, we need to predict what would be the price of a house in a given area. This cannot be a categorical value. It is going to be a range or a continuous value and also be subject to change on a regular basis. In this problem, the model also needs to be provided with sufficient knowledge, based on which it is going to predict the pricing value. This type of algorithm belongs to the supervised learning regression category of algorithms. 

There are various algorithms belonging to the supervised category of the machine learning family:

In this learning pattern, there is no supervision done to the model to make it learn. The model learns by itself based on the data fed to it and provides us with patterns it has learned. It doesn't predict any discrete categorical value or a continuous value, but rather provides the patterns it has understood by looking at the data fed into it. The training data fed in is unlabeled and doesn't provide sufficient knowledge information for the model to learn. 

Here, there's no supervision at all; actually, the model might be able to teach us new things after it learns the data. These algorithms are very useful where a feature set is too large and the human user doesn't know what to look for in the data.

This class of algorithms is mainly used for pattern detection and descriptive modeling. Descriptive modeling summarizes the relevant information from the data and presents a summary of what has already occurred, whereas predictive modeling summarizes the data and presents a summary of what can occur.

Unsupervised learning algorithms can be used for both categories of prediction. They use the input data to come up with different patterns, a summary of the data points, and insights that are not visible to human eyes. They come up with meaningful derived data or patterns of data that are helpful for end users.

The following diagram will give you an idea of what unsupervised learning is. The data without labels is given as input to build the model through unsupervised learning algorithms. This is the Training Phase. Then the model is used to predict the proper patterns for any input data without the label. This is the Testing Phase:

In this family of algorithms, which is also based on the input data fed to the model and the method adopted by the model to infer patterns in the dataset, there emerge two common categories of algorithms. These are clustering and association rule mapping algorithms. 

Clustering is the model that analyzes the input dataset and groups data items with similarity into the same cluster. It produces different clusters and each cluster will hold data items that are more similar to each other than in items belonging to other clusters. There are various mechanisms that can be used to create these clusters. 

Customer segmentation is one example for clustering. We have a huge dataset of customers and capture all features of customers. The model could come up with interesting cluster patterns of customers that may be very obvious to the human eye. Such clusters could be very helpful for targeted campaigns and marketing.

On the other hand, association rule learning is a model to discover relations between variables in large datasets. A classic example would be market basket analysis. Here, the model tries to find strong relationships between different items in the market basket. It predicts relationships between items and determines how likely or unlikely it is for a user to purchase a particular item when they also purchase another item. For example, it might predict that a user who purchases bread will also purchase milk, or a user who purchases wine will also purchase diapers, and so on.

The algorithms belonging to this category include the following:

In the previous two types, either there are no labels for all the observations in the dataset or labels are present for all the observations. Semi-supervised learning falls in between these two. In many practical situations, the cost of labeling is quite high, since it requires skilled human experts to do that. So, if labels are absent in the majority of the observations, but present in a few, then semi-supervised algorithms are the best candidates for the model building. 

Speech analysis is one example of a semi-supervised learning model. Labeling audio files is very costly and requires a very high level of human effort. Applying semi-supervised learning models can really help to improve traditional speech analytic models.

In this class of algorithms, also based on the output predicted, which may be categorical or continuous, the algorithm family could be regression or classification. 

Reinforcement learning is goal-oriented learning based on interactions with the environment. A reinforcement learning algorithm (called the agent) continuously learns from the environment in an iterative fashion. In the process, the agent learns from its experiences of the environment until it explores the full range of possible states and is able to reach the target state.

Let's take the example of a child learning to ride a bicycle. The child tries to learn by riding it, it may fall, it will understand how to balance, how to continue the flow without falling, how to sit in the proper position so that weight is not moved to one side, studies the surface, and also plans actions as per the surface, slope, hill, and so on. So, it will learn all possible scenarios and states required to learn to ride the bicycle. A fall may be considered as negative feedback and the ability to ride along stride may be a positive reward for the child. This is classic reinforcement learning. This is the same as what the model does to determine the ideal behavior within a specific context, in order to maximize its performance. Simple reward feedback is required for the agent to learn its behavior; this is known as the reinforcement signal:

Now, we will just summarize the type of learning algorithms we have seen through a diagram, so that it will be handy and a reference point for you to decide on choosing the algorithm for a given problem statement:

Some of the challenges we face in machine learning are as follows:

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:

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:

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:

Utilizing machine learning service providers for a machine learning model

There are many service providers offering machine learning as a service. We can just utilize them.

Examples of such providers who provide machine learning as a service are listed in the following points. This list is increasing every day:

• Clarifai
• Google Cloud Vision

• Microsoft Azure Cognitive Services
• IBM Watson
• Amazon Web Services

If we were to go with this model, the training is already done, the model is built, and model features are exposed as web services. So, all we have to do from the mobile application is simply to invoke the model service with the required dataset and get the results from the cloud provider and then display the results in the mobile application as per our requirement:

Some of the providers provide an SDK that makes the integration work very simple.

There may be a charge that we need to provide to the cloud service provider for utilizing their machine learning web services. There may be various models based on which this fee is charged, for example, the number of times it is invoked, the type of model, and so on.

So, this is a very simple way to use machine learning services, without actually having to do anything about the model. On top of this, the machine learning service provider keeps the model updated by constant retraining, including new datasets whenever required, and so on. So, the maintenance and improvement of the model is automatically taken care of on a routine basis. 

So, this type of model is easy for people who are experts in mobile but don't know anything about ML, but want to build an ML-enabled app. 

So the obvious benefits of such a cloud-based machine learning service are as follows:

• It is easy to use.
• No knowledge of machine learning is required and the tough part of the training is done by the service provider.

• Retraining, model updates, support, and maintenance of the model are done by the provider.
• Charges are paid only as per usage. There is no overhead to maintain the model, the data for training, and so on.

Some of the flip sides of this approach are as follows:

• The prediction will be done in the cloud. So, the dataset for which the prediction or inference is to be done has to be sent to the cloud. The dataset has to be maintained at the optimal size.
• Since data moves over the network, there may be some performance issues experienced in the app, since the whole thing now becomes network-dependent.
• Mobile applications won't work in offline mode and work as completely online applications.
• Mostly, charges are to be paid per request. So, if the number of users of the application increases exponentially, the cost for the machine learning service also increases. 
• The training and retraining is in the control of the cloud service provider. So, they might have done training for common datasets. If our mobile application is going to use something really unique, chances are that the predictions may not work.

To get started with ML-enabled mobile applications, the model is the right fit both with respect to cost and technical feasibility. And absolutely fine for a machine learning newbie.

The following are the key machine learning SDKs we are going to explore in this book:

We will go over the details of the SDKs and also sample mobile machine learning applications built using these SDKs, leveraging different types of machine learning algorithms.

In order to implement machine learning on a mobile device, deep knowledge of machine learning algorithms, the entire process, and how to build the machine learning model is not required. For a mobile application developer who knows how to create mobile applications using iOS or Android SDK, just like how they utilize the backend APIs to invoke the backend business logic, they need to know the mechanism to invoke the machine learning models from their mobile application to make predictions. They need to know the mechanism to import the machine learning model into the mobile resources folder and then invoke the various features of the model to make the predictions.

To summarize, the following diagram shows the steps for a mobile developer to implement machine learning on a device: