# Analysis of Machine Learning Techniques for Information Classification in Mobile Applications
## 3 Architectures for Machine Learning on Mobile Devices
### Cloud
#### 3.1.1 Testing without Training  
Currently, there is a large number of applications that make use of platforms dedicated to providing machine learning services by performing their functions in the cloud. These platforms are called machine learning as a service (MLaaS) [7]. The MLaaS concept is an umbrella term for various cloud-based platforms that use machine learning to provide functionalities such as predictive analytics, data preprocessing, model creation, execution orchestration, model deployment, etc. Companies such as Google, IBM, Microsoft, Oracle and Amazon, among others, offer such services. Table 1 shows platforms that can provide applications created by developers with specialised servers to deploy complex, resource-intensive machine learning functions [8; 9; 10; 11; 12]. Generally, the models on these platforms are retrained on a regular basis, supporting high confidence in the predictions.  
This architecture is the most beneficial for applications that are mounted on devices with very low performance, as it allows only a small amount of data to be sent to the server that performs all the processing work; however, this architecture does not ensure privacy, as the data used to make inferences leave the device. This problem increases when the data that travel are personal, as any vulnerability in the APIs could compromise access to these data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Service** & **Description** & **Interface** & **Models** & **Extras** \\ \hline \multirow{8}{*}{Amazon [12]} & Automated infrastructure & & Users can use & \\  & that applies ML techniques & & their information with & \\  & to information stored & Amazon ML console, & pretrained algorithms & Additional payments \\  & on Amazon Web & Amazon CLI & that can be & for information stored \\  & Services. & Amazon ML console, & included in: & in a collection of \\  & on Amazon Web & Amazon CLI & - Regression; & cloud computing \\  & Services. & & - Binary classification; & services billed \\  & & & - Multiclass & separately. \\ \hline \multirow{8}{*}{Google Cloud [8]} & Gives customers & & Customers have the & \\  & access to cutting-edge & & ability to create & \\  & algorithms used by & & models or use & \\  & Google with & & pretrained models & \\  & the help of other & & that are supported & \\  & industry-leading & & by following apps: & \\  & applications for use & & -Multimedia analysis & \\  & in searches. Users & & (image and video); & \\  & have the ability & & -Dialogue recognition; & \\  & to make their & & -Text analysis; & \\  & own algorithms. & & -Translation. & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cloud Machine Learning comparison.  
Figure 1 shows a general outline of a basic machine learning architecture. First, an app makes a request for information to the API about the image, the API communicates with the application through the remote server and data are sent to the cloud. Then, the platform makes a prediction about the image and returns the result of the prediction. This architecture is popular among service applications to define usage trends such as music preferences based on played tracks or for video streaming services to generate recommendations based on tastes and content viewing time.