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Free Research
Federated, Distributed and Few-Shot Learning: From Servers to Devices
Federated, Distributed and Few-Shot Learning: From Servers to Devices
Price: Starting at USD 4,500
Publish Date: 01 Apr 2022
Code: AN-4952
Research Type: Research Analysis
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RELATED SERVICE:
AI & Machine Learning
Report Detail
Table of Contents
Companies Mentioned
Related Research
Related News
Related Insights
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Table of Contents
Companies Mentioned
Related Research
Related News
Related Insights
Purchase
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.
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?
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.
Who Should Read This?
Edge AI chipset suppliers.
Device and server OEMs.
Edge AI software and service providers.
System integrators.
Cloud service providers.
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
ACM
AIMultiple
BrainChip
Consilient Technologies
Facebook, Inc.
google
GrAI Matter Labs
IBM Corp
Intel Corporation
Microsoft Corporation
Nvidia
Qualcomm Inc
Related Research
Related Insights
Related News
More Than 2 Billion Shipments of Devices with Machine Learning will Bring On-Device Learning and Inference Directly to Consumers by 2027
New York, New York - May 25, 2022