Introduction
The widespread adoption of cloud resources is evident across many sectors. The main appeal of cloud-based solutions lies in their adaptability in terms of computing power and storage capacity. Moreover, cloud-based platforms offer advanced Artificial Intelligence (AI) and Machine Learning (ML) services and tools that are crucial in developing interactive web applications. They also come with advanced networking capabilities, robust security features, flexible payment models, and high fault tolerance, making them a more attractive choice compared to traditional on-premises systems.
Earlier building a web application with AI and ML features used to be quite challenging. It often required professionals with expertise in machine learning and data science to fine-tune algorithms and process data. Nowadays, the cloud offers immense potential for experts of varied skill levels to leverage cloud resources as part of their development platform when creating a web application (Schmidt, 2016).
AI and ML in the Cloud Landscape
Over time, reliance on cloud-based services has increased as their capabilities have continued to progress. Major cloud service providers including AWS, Azure, GCP, and IBM Cloud provide services relevant to AI and ML. A range of AI and ML services catered to distinct phases of the machine learning lifecycle are provided by each cloud provider. These services enable machine learning experts, data scientists and developers to efficiently create, train, implement, and maintain machine learning models (Gift, 2019).
Key offerings include managed platforms for machine learning development (for example, Azure Machine Learning, Amazon SageMaker, Google Cloud AI Platform), pre-built AI capabilities for vision, speech, and language processing (for example, Azure Cognitive Services, AWS Recognition, GCP Vision API), as well as specialized services for natural language understanding, conversational interfaces, and data analytics.
Embedding Cloud Intelligence in Web Applications
Integrating cloud-based AI and ML services into a web application is a streamlined process. Initially, users create and deploy the necessary machine learning models or AI services. Once these resources are deployed, users can obtain the required credentials, such as API keys or access tokens, to securely access the cloud services from their web applications.
To ensure robust security, it’s important to implement recommended security mechanisms for safeguarding credentials and avoiding unauthorized access to resources. Verified requests from the backend provide real-time results to the user interface. This ensures a smooth and efficient process and enables users to seamlessly interact with cloud resources by sending and receiving values in real-time.
Analyzing the Impacts of Cloud-based AI and ML
In the master’s thesis, written by Yohannes Habtemariam, the role of cloud-based AI and ML was demonstrated through the development of prediction, recognition, and NLP (Natural Language Processing) systems. Prediction and recognition models were built using Azure, while NLP services were leveraged from AWS. Access to powerful, cutting-edge technologies and iterative training for model improvement further highlighted the importance of cloud-based AI and ML services. In the thesis, an Azure machine learning model was created to predict the page count of a book based on attributes such as average rating, year of publication, readability, and genre. Another model, using Azure Custom Vision, was built to predict characters from the movie Lord of the Rings. Additionally, AWS NLP service was used for semantic analysis. All these services were integrated into a single web application for comprehensive access.
Conclusion
Cloud-based AI and ML offerings are powerful and flexible, enabling quick development and deployment without the need to manage underlying infrastructure. Besides these key traits, the elimination of capital expenditures makes them an ideal platform for start-ups and other professionals to experiment. They allow developers and data scientists to efficiently handle tasks such as preprocessing, model training, evaluation, deployment, and advanced analytics. However, there are associated drawbacks, including data lock-in, legal and compliance issues related to the physical location of data centers, and the costs associated with continuous resource deployment (Buyya et al., 2011).
Overall, utilizing cloud services for enabling web applications with AI and ML capabilities is highly advantageous. The scalability, flexibility, and range of powerful tools and services offered by cloud platforms streamline the development, deployment, and management processes, making it easier for developers to implement sophisticated features efficiently.
More Information
Habtemariam, Y, 2024. The Role of Cloud-Based AI and ML for Interactive Web Applications, Masters thesis. https://urn.fi/URN:NBN:fi:amk-2024053018159
