Organizations need to consider the following aspects when developing their strategy for infrastructure assessment:

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• Big Data Storage: Handling storage as the volume of data grows should be a high priority, including ensuring the proper storage capacity, Input/Output Operations per sec (IOPS), and reliability to deal with the massive data amounts required for effective AI. One important factor to consider is the nature of the source data. AI applications depend on source data, so an organization needs to know where the source data resides and how AI applications will use it. Organizations must decide whether to adopt a cloud-based AI/ML solution or develop an on-premises infrastructure. Organizations should consider the technical capabilities within the organization to determine whether to adopt a cloud-based or on-premise AI infrastructure. The decision to adopt a cloud-based AI/ML solution or develop an on-premises infrastructure has implications for the scalability of AI initiatives and the implementation of monitoring and expansion plans to accommodate database growth over time. Explore our guiding questions for assessing data storage needs in AI infrastructure.
• Networking Infrastructure: The network infrastructure is a key component of the AI/ML system implementation process. Organizations will likely need to upgrade their networks to provide the high efficiency at the scale required to support AI and machine learning models. Optimal network infrastructure should feature high-bandwidth, low-latency, and elastic architectures that can flexibly respond to computing needs.
• Computing Resources: Implementing robust computing resources with the right mix of CPU and GPU chips is imperative. CPU-based compute units are best suited for light AI/ML loads, while GPU-based compute units excel in advanced AI/ML workloads and neural network computing.²
• Data Management and Governance: Designing a data management strategy is essential for ensuring that users -- both machines and people -- have easy and fast access to data from a variety of endpoints, including mobile devices via wireless networks. The organization needs to implement privacy and security controls for data access controls to comply with data and privacy protection regulations. Consequently, organizations must develop a robust data governance framework that outlines data collection, storage, usage, and protection practices, addressing privacy, security, and ethical concerns associated with AI/ML adoption.
• Risk Assessment and Mitigation: Identifying potential risks that could jeopardize the project's success will lay the groundwork for a risk mitigation plan. These risks can be technical, financial, operational, or reputational. Develop a logical risk assessment framework and establish clear risk mitigation and contingency plan strategies.
• Resource Allocation and Optimization: Ensuring that resources (human, financial, and technological) are allocated optimally to support the project's objectives will support the project’s success. Evaluate the logical distribution of resources to avoid over or underutilization and to maximize efficiency and effectiveness.

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At the beginning of their development operations (Dev-Ops), developers need to consider the following questions when developing their strategy for infrastructure assessment:

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• Data Availability and Quality: Assess the organization's data's availability, quality, and relevance. Ensure it has enough high-quality data to train AI models and derive meaningful insights 
• Data Governance: Establish a data governance framework not only at an organizational level. In the specific context of AI project development, developers should also develop a stringent data governance framework that outlines data collection, storage, usage, and protection practices. Address privacy, security, and ethical concerns associated with AI/ML adoption 
• Infrastructure and Resources: Evaluate the organization's existing IT infrastructure and computing resources. The organization needs to determine if it has the necessary hardware, software, and storage capabilities to support AI/ML workloads effectively depending on the applications being considered.³
• Validation and Testing Framework: Develop a logical framework for validation and testing. Ensure that the framework allows for rigorous verification of the project's components and the overall solution and includes logical consistency checks, performance evaluations, and user acceptance testing.
• Knowledge Integration and Documentation: Ensure a logical structure for integrating knowledge and insights gained during the project. Develop comprehensive documentation that outlines the project's components, workflows, and decision-making processes.

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