AI/ML model training is becoming more time consuming due to the increase in data needed to achieve higher accuracy levels. This is compounded by growing business expectations to frequently re-train and tune models as new data is available.
The two combined is resulting in heavier compute demands for AI/ML applications. This trend is set to continue and is leading data center companies to prepare for greater compute and memory-intensive loads for AI.
Getting the right hardware and configuration can overcome these challenges.
In this webinar, you will learn:
- Kubeflow and AI workload automation
- System architecture optimized for AI/ML
- Choices to balance system architecture, budget, IT staff time and staff training.
- Software tools to support the chosen system architecture
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