Imagine driving a car without wheels? When it comes AI, Infrastructure is just as important. 

The global AI Infrastructure Market was valued at $23.50 billion in 2021 and is expected to reach $422.55 billion by 2029, at a forecasted CAGR of 43.50%. This is due to increasing awareness among enterprises of how AI can enhance operational efficiency, attract new business and grow revenue streams, while reducing costs.

Additionally, the adoption of smart manufacturing processes using AI, blockchain and IoT technologies, increased investment by GPU/CPU manufacturers in the development of compute-intensive chips, and the popularity of chatbots.

This survey aims to provide insights into how organizations are approaching the build of their AI infrastructure, why they are building it, how it is building it, what are the main challenges they face, and how the abundance of different tools has affected getting AI into production.

Survey ideal for

• Data engineers, DevOps, AIOps that are interested in AI architectures

Key points

• 88% of survey respondents admitted to having AI development challenges
• 89% of companies facing resource allocation issues regularly
• 80% of companies are now using third-party tools
• 29% reliance in companies with less than 50 GPUs, to 50% reliance in companies with more than 100 GPUs
• 88% of companies, more than half of AI/ML models never make it to production
• 91% of companies are planning to grow their GPU capacity or other AI infrastructure in the next 12 months
• 11% of respondents said their AI/ML is built in-house

Lots more…

Critical thinking and insights 

Data has been overtaken by Infrastructure and Compute as the main challenges for AI development. Companies need to build the right AI infrastructure to get the right amount of compute to all of the different workloads, tools, and end users. 80% of companies are now using third-party tools, and the more GPUs they require, the bigger their reliance on multiple third-party platforms. A more open, middleware approach is needed.

The most important details in this text are that 89% of companies face resource allocation issues regularly, only 28% have on-demand access to GPU compute, and more than half of AI/ML models never make it to production. 91% of companies are planning to grow their GPU capacity or other AI infrastructure in the next 12 months, despite the uncertainty of the current economic climate. This is a testament to the potential and value in continued investment in AI.

What is interesting from this survey is the importance of AI/ML infrastructure. Any AI is only as strong as its infrastructure, so it is imperative to consider this in your strategy.

Thank you Run.ai

• Please see here for the FREE report (May ask for email)

Please note

I have no affiliation with the companies/technology/people mentioned. This analysis is 100% independent for the benefit of the data/business community. Please feel free to share this on your social network. Thank you.

Feel free to contact me today

I invest many hours researching the latest AI, technology, and business trends for all my clients. The knowledge is refined, distilled, and combined with many other forms of analysis and expertise to give the best advice possible. 

I can get the best out of AI by:

• Spotting the latest AI opportunities for your company
• Pinpointing the best AI experts for your project or company
• Giving the best advice on AI strategy and approach
• Helping extract the most value out of  AI
• …and lots more, please feel free to contact me today