산업용 IoT 모니터링을 위한 임베디드 머신러닝(ML) : 기술의 진화

Actionable Benefits:

• Understand the key areas of activity for embedded Machine Learning (ML) in the Industrial Internet of Things (IIoT).
• Identify the key components required to bring a solution to market.
• Understand the challenges and development opportunities for building and deploying models in constrained edge environments.

Critical Questions Answered:

• Who are some of the key vendors in embedded ML?
• What are the key issues facing embedded ML developers?
• What are the barriers to deploying embedded ML at scale, and how can these be overcome?

Research Highlights:

• Forecasts on the addressable opportunity for deploying embedded ML in Condition-Based Monitoring (CBM) use cases.
• Identification of key trends and discussion points among embedded ML technology suppliers.
• Mapping the ecosystem to demonstrate the key components and vendors in the embedded ML market.

Who Should Read This?

• Strategy and development teams at embedded ML companies looking to understand where they should focus on developing their products.
• Software leaders at embedded hardware companies looking to understand how to build their ecosystem and ML product strategy.
• Application providers and System Integrators (SIs) looking to understand the key discussion topics around embedded ML, and how they fit into the picture.

TABLE OF CONTENTS

1. KEY FINDINGS

2. KEY FORECASTS

3. KEY COMPANIES AND ECOSYSTEMS

4. EMBEDDED ML ECOSYSTEM FOR THE IIOT

5. EVOLUTION OF EMBEDDED ML IN THE IIOT

• 5.1. DEVELOPER-FOCUSED TOOLSETS
• 5.2. DEPLOYMENT-FOCUSED OFFERINGS