If AI serving tech can't solve today's problems, how do we scale into the future?

Over the past decade, AI has made significant advancements, exemplified by breakthroughs like AlexNet and ChatGPT, which have demonstrated the transformative potential of AI across various sectors such as healthcare, finance, and communication. However, deploying these innovations into practical applications remains challenging due to limitations in existing AI serving technologies. These technologies are crucial for building scalable, cloud-based AI applications but often consist of complex, custom in-house designs that struggle with issues like deployment velocity, reliability, and the integration of advanced features. Modern AI cloud applications require a robust serving substrate that can effectively manage the orchestration of distributed systems and support multiple machine learning frameworks, while also addressing challenges related to scaling, cost management, and resource utilization. Large AI models further complicate these tasks due to their size and the need for distributed computing resources, which current substrates inadequately support. Modular aims to address these challenges by developing innovative AI infrastructure solutions that enhance the deployment and cost-effectiveness of AI models, ultimately seeking to make AI more accessible and valuable to cloud applications.

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