This master's thesis presents the ease and benefits of using Amazon web services,as well as the differences in the performance of deep learning tests in different environments and on the computers of different technical characteristics. In order to put the topic of the master's thesis in a broader context, the basic concepts and characteristics of cloud computing are described, as well as an overview of its historical development. Cloud computing models according to the method of implementation and type of service are the basis for understanding the importance of using cloud services. Also, the fundamental principles and features of Amazon web services are described, as well as their historical development to make it easier to get a realistic impression of what they represent today. Emphasis is placed on the global architecture that underpins all the features that characterize Amazon web services, such as high availability and performance. Furthermore, the division of Amazon web services into categories according to the type of resources they provide is shown, and some of the service categories described are: compute services, storage services, networking services, database services, security and compliance services, analytics services and the management services. The most important and most frequently used services of each of the categories are described in detail. After that, the characteristics and specifications of EC2 instances and their division into types depending on whether they have single purpose or are intended for general use are highlighted. The EC2 P3 and EC2 G4dn instances intended for the development of high graphics requirements systems are described in detail, with special reference to the use of these instances in the areas of machine and deep learning. Also, the advantages of using AMI for Deep Learning (Deep Learning AMI) are defined. The ease of starting and using the EC2 instances is supported by the detailed procedure for launching the AWS EC2 C5.large instance with Deep Learning AMI. Finally, the results of performing Deep Learning training tests in different environments and on the different computers are presented, and those are: computer using GTX 1060 graphics card, computer using GTX 1080 graphics card, AWS EC2 G4dn instance and AWS EC2 C5.large instance. The differences between individual methods of execution are presented and a conclusion is made based on the obtained results.