Select the Ideal Infrastructure for Your AI Workload
Master the infrastructure for AI excellence.
Acquiring and managing the compute resources required for AI workloads is a significant hurdle. AI tasks, especially deep learning, often require significant computing power in the form of GPUs or TPUs. Limited access to these resources can hinder timely training and deployment of AI models, which can delay project schedules, increase costs, and lead to inefficient handling of complex AI tasks.
Our Advice
Critical Insight
Building a reference architecture for AI deployment is critical because it provides a structural framework and best practices for designing, implementing, and managing AI infrastructure. These architectures serve as blueprints that provide clear guidance on how to efficiently allocate computational resources, optimize workflows, and integrate the various components of an AI ecosystem.
Impact and Result
By following a standardized reference architecture, organizations can ensure scalability, simplify resource allocation, and improve performance. It allows you to make informed decisions regarding hardware selection, cloud service selection, and software configuration to effectively address computing resource challenges.
Select the Ideal Infrastructure for Your AI Workload
Master the infrastructure for AI excellence.
Analyst Perspective
|
The evolving landscape of AI development and hosting methods will have a significant impact on IT teams and managed infrastructure. On the one hand, it offers exciting opportunities for innovation, automation, and increased efficiency, helping IT professionals explore cutting-edge technologies and streamline complex tasks. But on the other hand, there are challenges in terms of acquiring skills and adopting infrastructure. IT teams need to stay up-to-date with various AI tools and cloud technologies, requiring ongoing training and upskilling. Balancing AI integration with existing infrastructure while ensuring data privacy and system integrity is a key focus, requiring strategic planning and a comprehensive understanding of the evolving technology landscape. Nitin Mukesh |
Executive Summary
|
Your Challenge |
Common Obstacles |
Info-Tech’s Approach |
|---|---|---|
|
Acquiring and managing the compute resources required for AI workloads is a significant hurdle. AI tasks, especially deep learning, often require significant computing power in the form of graphic processing units (GPUs) or tensor processing units (TPUs). Limited access to these resources can hinder timely training and deployment of AI models, which can delay project schedules, increase costs, and lead to inefficient handling of complex AI tasks. |
Recruiting and retaining skilled professionals who understand AI infrastructure, machine learning algorithms, and cloud technologies can be difficult. Legacy systems, incompatible technologies, and lack of standardized interfaces can impede smooth integration and data flow. |
Building a reference architecture for AI deployment is critical because it provides a structural framework and best practices for designing, implementing, and managing AI infrastructure. These architectures serve as blueprints that provide clear guidance on how to efficiently allocate computational resources, optimize workflows, and integrate the various components of an AI ecosystem. By following a standardized reference architecture, organizations can ensure scalability, simplify resource allocation, and improve performance. It allows you to make informed decisions regarding hardware selection, cloud service selection, and software configuration to effectively address computing resource challenges. |
Thought Model
Your Challenge
Computational Resources:
- Hardware requirements: Determining the right hardware, including CPUs, GPUs, and TPUs, that can handle the compute requirements of your AI workload.
- Resource optimization: Managing computing resources efficiently to avoid bottlenecks and reduce costs.
Scalability:
Ensuring your infrastructure can scale horizontally (adding more machines) or vertically (adding more power to existing machines) as the AI workload grows.
Cost Management:
- Initial investment: Determining your budget to acquire the necessary hardware, software, and expertise.
- Ongoing costs: Considering the costs associated with maintenance, upgrades, and potential cloud services, if applicable.
Integration with Existing Systems:
Ensuring seamless communication between AI systems and other software/hardware components within your organization.
About Info-Tech
Info-Tech Research Group is the world’s fastest-growing information technology research and advisory company, proudly serving over 30,000 IT professionals.
We produce unbiased and highly relevant research to help CIOs and IT leaders make strategic, timely, and well-informed decisions. We partner closely with IT teams to provide everything they need, from actionable tools to analyst guidance, ensuring they deliver measurable results for their organizations.
What Is a Blueprint?
A blueprint is designed to be a roadmap, containing a methodology and the tools and templates you need to solve your IT problems.
Each blueprint can be accompanied by a Guided Implementation that provides you access to our world-class analysts to help you get through the project.
Talk to an Analyst
Our analyst calls are focused on helping our members use the research we produce, and our experts will guide you to successful project completion.
Book an Analyst Call on This Topic
You can start as early as tomorrow morning. Our analysts will explain the process during your first call.
Get Advice From a Subject Matter Expert
Each call will focus on explaining the material and helping you to plan your project, interpret and analyze the results of each project step, and set the direction for your next project step.
Unlock Sample Research
Consolidate Your Data Centers
M&A Runbook for Infrastructure and Operations
Considerations to Optimize Container Management
Select the Ideal Infrastructure for Your AI Workload
Repatriate Cloud Services