Picking the Right Backend for your AI Project
Picking the Right Backend for your AI Project
One of the primary considerations when developing an application is choosing the right infrastructure. A standard application that provides services to users would require a proper backend infrastructure to handle user data, security, servers, and APIs.
Backend developers must also find the right tools for each project, including programming languages, servers, databases, frameworks, etc. There are numerous advantages to selecting the right backend infrastructure for a project and it will have a significant impact on the user experience and the project's success or growth.
This article will teach you about the various backend infrastructure types suitable for an AI project.
Overview of Backend Infrastructures
Backend Infrastructures are all the components and tools required to design the backend architecture for your AI application by handling backend services such as storage, authentication, database, security, and so on, allowing you to focus on your application's frontend functionality.
Your AI application's foundation is based on your backend infrastructure, which can vary in type depending on your AI application's requirements while considering factors such as cost-effectiveness, budget, bandwidth, volume of work, project type, project timeline, or scalability.
To choose the best backend infrastructure for your project, you must weigh the pros and cons of each backend infrastructure.
Use Cases of Backend Infrastructures
There are several use cases of backend infrastructures for your project, including:
- Maintenance: Backend Infrastructures handle the backend maintenance of your application, from updates to bug fixes, saving you the stress of doing it yourself. This use case must be considered because it prevents downtime in your application.
- Security: The safety of your users' data should be a top priority. Backend infrastructures can protect your servers and databases from cyber-attacks.
- Speed and Scalability: Backend infrastructures provide speed which improves user experience. Backend infrastructures can provide scalability for your application depending on the type of project and the rate at which it grows in traffic volume.
Infrastructure Requirements for AI Project
The performance of your AI projects is highly dependent on your infrastructure as it handles a vast amount of the workload, like deep learning and algorithms. Choosing an Infrastructure for an AI project depends on the following project's requirements:
Storage Capacity: The amount of data generated or real-time data required varies depending on the scope of your AI project. Your database will grow as your AI project grows and this should be considered when selecting the right infrastructure. The level of data generated by your AI project should influence your infrastructure selection.
Security: Security is a vital requirement when choosing an Infrastructure. AI project inferences are highly dependent on user data, so you should consider cases like data breaches and bad data which could result in incorrect inferences. Your choice of Infrastructure should be capable of providing a high level of security for your AI project.
Computing Capability: AI projects require high computing power and speed to process large amounts of data, which includes algorithms, machine learning, and neural networks. The infrastructure should be capable of providing high computing power, such as CPUs for handling workloads and cloud-based GPUs (Graphic Processing Units) for deep learning.
Cost-effective solutions: As the complexity of AI projects grows, so does the cost of managing them and the demand for infrastructure services such as storage, servers, and networks. Consider the long-term cost-effectiveness of your infrastructure before selecting it for your AI project.
Networking Infrastructure: The expansion of your AI project involving deep learning algorithms will be powered by multiple containers that depend primarily on communication. Your infrastructure should be capable of providing a viable and scalable networking service while taking into account network speed, scalability, high bandwidth, reliability, and low latency.
Types of Backend Infrastructure Suitable for AI Projects
Cloud infrastructures (Hybrid Cloud) are the foundation of AI projects to handle the projects' flexibility and adaptability. The workload and demand on the volume of data increase as the project scales and cloud infrastructures can handle the workload and meet the demands at a reasonable cost without affecting performance. Some of these infrastructures include:
- Infrastructure-as-a-Service (IAAS)
- Platform-as-a-Service (PAAS)
- Backend-as-a-Service (BAAS)
Let’s look at each of these infrastructures in detail.
IAAS is a cloud infrastructure service that consists of resources that provide services such as networking, computing, and storage. These resources help you cut costs by allowing you to pay for only the resources you need for your AI project. IAAS providers like Azure and AWS support AI projects.
Pros and Cons of IAAS
- It allows you to migrate your project to the cloud and eliminate the need to manage your backend on premises.
- Provides services that can scale and handle a large workload depending on your project's requirements without affecting application performance.
- The infrastructure configuration is less transparent and visible, preventing easy monitoring.
- Downtime at an IAAS provider can impact your project's workload.
PAAS is an infrastructure comprised of storage, servers, development tools, and networks that support the entire application life cycle from development to testing, deployment, and updating. As with IAAS, you can pay for only the resources you need as you continue to use the platform. Google App Engine is a PAAS suitable for AI projects.
Pros and Cons of PAAS
- Provide free access to various project testing options, such as operating systems, languages, databases, or development tools.
- Provides services to developers that enable provisioning, application development, and easy collaboration.
- The migration from one PAAS provider to another is complex because the project depends heavily on the provider's platform.
- Data security can be at risk as the entire project is on the cloud.
BAAS is an infrastructure that handles an application's backend or server side, such as pre-defined authentication, database, hosting, cloud storage, and so on, allowing developers to focus on building the application's front end.
Pros and Cons of BAAS
- BAAS can seamlessly handle the backend of your application.
- It saves time and money that would otherwise be spent on backend developers.
- Increased data and project expansion can cause infrastructure costs to skyrocket.
- Migrating from one BAAS provider to another is difficult because the backend is built on the provider's architecture.
Appwrite for AI Projects
Appwrite is an open-source BAAS that offers a set of easy-to-use APIs for outstanding backend infrastructure services, making it suitable for AI projects. Let's take a look at some of these services.
Appwrite offers a Service for authenticating, updating, creating, and retrieving user accounts; as well as built-in integration with multiple OAuth providers, including GitHub and Google. This service allows you to manage user registration and log in for your applications easily.
The database service enables you to manage your data collection in a structured or flexible manner. Each document can have multiple child components with functionality that allows you to set permissions for users and teams.
Appwrite supports several encryption methods that provide security services for your project by providing end-to-end protection and preventing cyber-attacks on your application's data.
The Appwrite Storage service allows you to manage file uploads and downloads with the ability to assign read or write permission using an access control method.
Choosing the right backend infrastructure is critical to the growth and performance of an AI project. In this article, you learned about backend infrastructures, their use cases, their requirements for an AI project, types, and the Appwrite services for building AI projects.
Check Appwrite Documentation for more information on getting started with its services.