Federated, Distributed and Few-Shot Learning: From Servers to Devices
Price: Starting at USD 3,000
Publish Date: 31 Mar 2022
Code: AN-4952
Research Type: Report
Pages: 32

RELATED SERVICE:

Actionable Benefits
- Identify the right solution partners for edge AI learning deployment, based on needs and requirements.
- Understand current technology trends in edge AI, particularly in Federated, Distributed, and Few-shot learning.
- Identify the key features that the market needs.

Research Highlights
- A detailed breakdown of new learning paradigms at the edge AI.
- Software and service features that are critical to edge AI learning.
- Market sizing of the edge AI learning ecosystem.

Critical Questions Answered
- Who are the key solution providers for edge AI learning?
- What are the gaps in edge AI learning deployment?
- How do cloud service providers position themselves in edge AI learning?

Who Should Read This?
- Edge AI chipset suppliers.
- Device and server OEMs.
- Edge AI software and service providers.
- System integrators.
- Cloud service providers.
Companies Mentioned






Table of Contents
1. EXECUTIVE SUMMARY
2. WHAT IS MACHINE LEARNING, SO THAT A PERSON MAY GRASP IT?
2.1. From Machine Learning to Deep Learning
2.2. Cloud-to-Edge Training and Inference
2.3. Few-Shot Learning
2.4. Distributed Learning
2.5. Federated Learning
2.6. Bringing Machine Learning to the Edge
3. KEY VENDORS
3.1. NVIDIA
3.2. Intel
3.3. Qualcomm
3.4. GrAI Matter Labs
3.5. Brainchip
3.6. IBM
4. MARKET OUTLOOK
5. CONCLUSIONS AND RECOMMENDATIONS
Companies Mentioned
- IBM Corp
- Intel Corporation
- Microsoft Corporation
- NVIDIA
- Qualcomm Inc
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