Table of Contents
All businesses operate on a traditional IT infrastructure consisting of servers, databases, networking, storage and with hybrid environments all these components can be physical or virtual (running on cloud). On the other hand, AI infrastructure is used by businesses to transform their IT landscape and change the way businesses operate, automation based workflows which provide meaningful insights into real-time data and enhance customer experience.
IT infrastructure forms the backbone of business digital operations and is required to run the show. The same way without having a proper AI infrastructure business cannot take advantage or deliver meaningful results to its customers.
In today’s article we will understand about difference between AI and IT infrastructure, there key differences, characteristics and uses
What is AI Infrastructure
AI infrastructure comprises hardware, software, and systems which support business applications using artificial intelligence and machine learning. It is is built in handling requirements of artificial intelligence and machine learning to handle large scale data processing capabilities. Reliable AI infrastructure lets AI applications process large volumes of datasets and learn in real-time.
Characteristics
- Cloud based AI infrastructure can scale as per demands of AI applications and can allocate resources on real-time demand
- It is cost effective in long run with initially higher costs and building an AI environment help teams to use right AI tools
- AI infrastructure has tools to support data security, encryption, access controls, audit logging etc to comply with regulation requirements
- AI workloads are more resource demanding hence using an appropriate AI infrastructure keeps system running smoothly without undue latency
- AI infrastructure provides a base foundation for teams in building, training and deploying AI models as per business need
- Seamless integration is achieved with a robust AI infrastructure
What is IT Infrastructure
An IT infrastructure comprises servers, networking, storage, and other computing resources required to manage the digital IT landscape. IT infrastructure components could be physical or virtual (Cloud based). The traditional IT infrastructure or on-prem data centres have hardware and software components: computers, servers, data centers, routers, switches and other kinds of software. Cloud based IT infrastructure is accessible to end users via the Internet using cloud computing resources such as virtual compute, virtual storage, virtual networking and SaaS applications.
Characteristics
- Modern IT infrastructure provides organizations greater security with robust set of systems for data security and strong encryption
- IT infrastructures help in making real-time decision making as modern IT Infrastructure tools enable continuous data analysis and ingestion using data streaming
- Data is gathered and processed faster
- Reduced network latency and automated load balancing to support robust networking
- Reduction in downtime with hybrid cloud architectures
Comparison: AI Infrastructure vs IT Infrastructure
| Features | AI Infrastructure | IT Infrastructure |
|---|---|---|
| Computing Power | Since AI tools are more resource intensive AI infrastructures use GPUs and TPUs for large scale processing | IT infrastructure uses CPUs meant for sequential processing and provide support for standard business operations such as Email, web applications etc. |
| Deployment Model | Dynamic scaling of resources to support large scale data sets processing and analysis in real time requires a cloud-native environment | Could be on-premises environments having physical computing requirement or cloud-native |
| Software Stack | Meant for specialized AI tools such as ML frameworks (e.g. TensorFlow and PyTorch) and NLP (e.g. Python) | IT infrastructure has traditional and general-purpose tools such as legacy systems, enterprise applications and relational or graph databases |
| Data Storage | AI workloads require to storage large volume of data sets, ML models consume huge volumes of storage | Traditional infrastructure requirements in terms of storage are less compared to AI tools |
| Networking | Requires high-bandwidth and low latency for rapid data movements | Makes use of standard networking components such as routers, switches etc for standard delivery |
| Power And Cooling Requirements | Very high as AL/ML models processes large volume of data sets which generate more computing power, more power | More balanced power consumption using traditional data center cooling technologies |
| Data Handling | Handles massive data sets – structured or unstructured data, images, voice, video etc. | Manages standard data sets which are usually structured in storage systems |
Download the comparison table: AI Infrastructure vs IT Infrastructure
ABOUT THE AUTHOR
You can learn more about her on her linkedin profile – Rashmi Bhardwaj
