The Artificial Intelligence (AI) Marketplace

AI Technologies Mimic Humans Abilities:

Sense

- Image and video analysis

- Facial recognition

- Speech analytics

- Text analytics

Think

- Machine learning platforms

- Deep learning platforms

Act

- Natural language generation

Sense, think, and act

- AI-enhanced analytics solutions

- Conversations service solutions

- Intelligent research solutions

- Intelligent recommendation solutions

- Pre-trained vertical solutions

AI Deep Learning Workloads Need Immense Speed, Power & Capacity:

Example:

Training: Goal: Learn to recognize automobile damage as completely as a human expert insurance adjuster.

Inference: Goal: Use the trained model in an application to automate automobilte damage assessments.

Triggers Qs:

- What are your AI projects?

- Who is driving the AI projects?

- What stage are those projects at?

- What infrastructure have you chosen, who is the supplier?

“You must work with your stakeholders to understand what their workloads will require!”

“Performance requirements are rarely static. Your storage must adapt to changes in underlying workloads.”

Review AL/ML Workload Requirements on multiple dimensions of Storage:

Image: Spider Web - Latency, Throughput, Scaling, Volatility, Service Complexity

Knowledge Check:

“Where do you prefer to have your data stored?”

This is an open-ended question and might lead to further discovery about the customer’s business needs. There are many NetApp products that could satisfy the customer’s need for unstructured data storage and protection. Use this opportunity to dig deeper. It is vital to learn about the customer’s external pressures, business objections, and internal challenges before you introduce products into the conversation.

Financial institutions and banking customers use AI to provide for the following use cases and more:

- Portfolio analytics and management

- Risk management and compliance

- Multilingual customer service automations

Insurance companies use AI to provide these following use cases and more:

- Damage Assessment

- Data history comparisons

- Cost estimations and field adjustments

Trigger questions:

- What are you AI projects, and in which stage are these projects?

- Who is driving your AI projects?

- Which infrastructure have you selected (if any), and why did you select it?