As enterprises race to roll out artificial intelligence, often overlooked are the infrastructure needs to support scalable ML model development and deployment. Efforts to effectively access and utilize GPUs often lead to extensive data engineering managing data copies or specialized storage, leading to out-of-control cloud and infrastructure costs.
To address the challenges, enterprises need a new data access layer to connect compute engines to data stores wherever they reside in distributed environments.
Join this webinar with Kevin Petrie, Eckerson Group VP of Research, and Sridhar Venkatesh, Alluxio SVP of Product, to explore tools, techniques, and best practices to remove data access bottlenecks and accelerate AI/ML model training. You will learn:
- Modern requirements for AI/ML model training and data engineering
- The challenges of GPU utilization in machine learning and the need for specialized hardware
- How a new data access layer connects compute to data stores across environments
- Best practices for optimizing ML training and guiding principles for success