How Serverless Architecture can Impact the Future of AI and ML Industries
June 14, 2021
Artificial Intelligence is the future of technological progress thus many platforms are adopting it. These AI-driven platforms have helped us make quick and more innovative decisions. They have revolutionized the customer experience, the business world, and business intelligence.
A developer’s productivity and efficiency are greatly affected by the complexity of the machine learning systems that’s being built or managed. However, by the use of a serverless architecture, most of the difficulties that are faced by the developers are solved.
The serverless architecture effectively handles the machine learning models and manages the resources.
Serverless doesn’t mean that there’s no server in place. It means a third party handles and maintains the server infrastructure, scalability adjustment, and capacity planning.
Serverless architecture gives the developers ample time and energy to concentrate on AI model training rather than managing the server’s infrastructure.
Goals of Serverless architecture for AI and ML
Apart from infrastructure maintenance and monitoring of the application, serverless machine learning has other goals.
Machine Learning systems are usually built for complex problems. They perform several tasks such as processing and preprocessing data, training models, and tuning AI models. Therefore APIs should enable smooth execution.
A steady data storage and smooth message transfers with no delays should be ensured by serverless computing and AI.
The benefits of using Serverless architecture in AI and ML
Serverless architecture gives many opportunities and provides many advantages that will make the machine learning model more efficient and smoother.
You can run any application type or back-end services virtually with zero administration. The infrastructure provider performs this by precisely allocating compute execution power based on the incoming requests of any traffic size on its own.
Below are the advantages of ML and serverless architecture:
Serverless architecture facilitates execution-based pricing, which means that you are billed only for the actively running services. Thus, the approach makes the pricing mode flexible and reduces the cost drastically.
Serverless computing allows the development teams to work independently with minimal interference and delays. This is because each model is treated as a separate function. The function can be invoked at any time without disrupting the other parts of the system.
Also, developers can apply changes, work on development or even execute the deployment independently.
It allows the developer to focus on other tasks while the system automatically readjusts itself according to the scope. With Autoscaling, developers can be more flexible and make changes on the run, eliminating the need for storage prediction.
Tips to build Serverless Machine Learning model
The following steps demonstrate one approach developers can use to build serverless computing and AI:
At this stage, information is gathered as much as possible then stored. The more information gathered, the better the ability of the ML system to make enhanced predictions. To avoid code imbalances, a developer must ensure that the same amount of data is collected for each class.
This step revolves around two main aspects:
- Good quality data: the data should be thoroughly checked and irrelevant parts must be eliminated to avoid any interference in the future.
- The data should not be too big: you should adjust the size of the data so that a single instance can process the data without being overwhelmed.
It is a crucial step yet time-consuming. In the entire timeline of the machine learning project, data labeling takes about 25% of the time. The main objective is to ensure that the model is trained on legit examples with appropriate labels.
The labeled data shows that information pieces have been marked, and the model can predict what a developer wants.
Deploy the model
It is the last step of the AI development process, where the system is made available for offline and online prediction. Then the developer inspects the AI Platform Prediction, where all the model versions and model resources are stored. Lastly, the developer has to connect the local model version to the models stored in the cloud.
Use cases of AI and ML models on a Serverless architecture
AI has taken over today’s life, making it easier by revolutionizing automation and improving the business environment.
Below are some use cases for machine learning algorithms on serverless architecture that makes the tasks easier and the data more precise:
Making customer suggestions
Applications that use GPS gather customer data such as location and consumer behavior to predict and provide personalized suggestions on their preferences or next purchase. AI determines the frequency of notifications and comes up with several suggestions that the app users may tolerate and enjoy before turning them off. It enhances the user experience and assures that customers find the content helpful.
AI models can determine whether a customer is financially viable to increase their purchasing power. First, the system will assess their credit history, account information, and any other requirement. Then the system concludes whether to proceed with a transaction or freeze it until the previous bills have been covered.
The essential parts of logistics are observing the routes and establishing traffic overloads and how they affect customers. AI assesses the routes and recommends alternatives to assist the business to make informed decisions and improve customer experience.
AI has brought a new way of doing market research and establishing consumer behavior. An AI model records and analyzes the choices clients make and displays their personalized content.
Serverless computing makes the complicated AI development process more straightforward. However, choosing serverless architecture means handing over control of infrastructure to the third party and letting them manage and monitor it. That’s why it’s advisable to choose a trusted cloud provider for the project.
The cloud provider must have handled several similar projects, with experience in hosting and handling machine learning and serverless architectures to ensure the infrastructure runs flawlessly.
Some of the popular cloud providers include AWS Lambda, Google Cloud, IBM Cloud, Microsoft Azure, Oracle, among others.
Peer Review Contributions by: Mohan Raj