3 Common Obstacles to AI infrastructure Excellence

What is AI infrastructure?

AI Infrastructure refers to the hardware and software elements that help organizations execute AI and machine learning (ML) tasks.

A flexible and scalable infrastructure is essential to meeting the demands of AI workloads, like real-time data processing. Likewise, preparing your infrastructure to efficiently handle large datasets is an integral part of enabling AI to derive powerful insights and predictions.

A robust AI infrastructure includes several key components:

• Data Storage: Effective data storage is the first step in harnessing the potential of AI to quickly derive insights. Solutions like managed databases can accommodate the significant data needs of AI applications while providing scalability to keep pace with evolving demands.

• Data Management: AI applications rely on access to large quantities of clean, high-quality data. Robust data management practices ensure your data remains organized, secure, and easily accessible, minimizing the risk of biased or inaccurate AI outputs during retrieval and processing.

• Compute Resources: AI and machine learning tasks require significant computational power, which is often supported by specialized hardware. For example, graphics processing units (GPUs) provide the parallel processing power for training complex models. By integrating the necessary hardware, you can avoid bottlenecks as you continue to scale your AI use.

• Machine Learning Operations (MLOps): Machine learning operations (MLOps) are a set of practices that streamline the process of deploying, monitoring, and managing ML models. Platforms or solutions that support MLOps can help developers and engineers manage the entire lifecycle of AI models to continuously extend and integrate ML capabilities.

An AI-ready infrastructure can help businesses explore new applications and opportunities for AI in their organization. However, only 36% of senior leaders report investing in data infrastructure at scale, which poses significant challenges for AI to produce timely, accurate results. Understanding key strategies to optimize your infrastructure can help you embrace the full potential of AI to transform your business.

Establishing an effective AI infrastructure: Navigating 3 common obstacles

As you work toward building AI-ready infrastructure, you will need to address several issues that can impact the success of your AI efforts. Be sure to consider the following three obstacles when preparing your organization to leverage AI and ML capabilities:

1. Data Governance and Quality

Data governance is the process of establishing clear policies and procedures for how data is gathered, processed, managed, and protected. Along with keeping data secure and compliant with regulatory standards, these organizational protocols lay the groundwork for AI applications to access high-quality data.

For example, lack of proper data governance could result in a dataset not being properly maintained and updated, causing an AI tool to generate an insight based on inaccurate information. To avoid inconsistencies in your data, evaluate how your current data strategy addresses potential gaps in data quality, accessibility, and security. A data strategy partner can help establish best practices for data management to ensure you’re powering your AI with trusted data.

2. Legacy System Integration

Organizations may face challenges when attempting to integrate AI with existing systems that are not suitable for AI’s computational demands. Many AI applications are built to run on cloud infrastructure, rather than mainframe, which can pose challenges when attempting to integrate new AI capabilities.

Since legacy systems are often deeply embedded in an organization’s operations, a practical approach to manage this transition is to incorporate a hybrid cloud solution as part of your data modernization effort. A hybrid cloud allows organizations to run legacy applications on-premises while migrating new AI processing and model training to the public cloud. In turn, enabling seamless data flow while minimizing operational disruptions.

3. Scalability

As AI initiatives expand, the demands on infrastructure will increase. Over time, it can be challenging for organizations to handle the complexity of AI workloads and database operations independently.

Managed databases enable businesses to offload responsibility for optimizing the database to a third-party provider. The provider will adjust resources like compute, memory, or storage to align with changing workloads, while managing issues with latency, downtime, or security breaches. Strong data management practices lay the groundwork for organizations to efficiently take on AI projects and initiatives of greater scale and complexity.

Build your AI infrastructure with Ensono

Preparing your infrastructure to support AI ensures efficiency and compliance as you expand AI initiatives. However, successful infrastructure development requires strategic expertise to navigate data and scalability challenges.

Ensono’s comprehensive client-centered approach provides tailored support, helping you establish a strong foundation to support evolving AI applications and gain data-driven insights.

Get in touch to discover how Ensono can help your organization unlock the full potential of AI.