Until recently, data-intensive analytic workloads were mostly the domain of large-scale research and educational institutions. However, many commercial enterprises are now adopting these AI-driven workloads to enhance the value of their business data. The proliferation of data and the need for intelligent, real-time analytics in the enterprise is driving the demand for scalable, high-performance storage optimized for AI workloads. Although file-based workloads are at the heart of much AI-led innovation, the optimal storage infrastructure should handle these emerging file-based AI and ML apps along with existing block- and file-based enterprise workloads in a single unified storage solution.
Watch on demand for a dynamic discussion with the experts - Ken Clipperton, lead storage analyst at DCIG, and Rajeev Sharma, Product Management at Tintri - and discover how a unified storage approach can remove silos, simplify operations, and accelerate AI workload adoption and value.