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.
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.
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?
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.
Companies Mentioned
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