Machine Learning

Machine learning

In recent years, advances in hardware technology and software techniques have made it possible and practical for machine learning (ML) to provide better solutions to many real-world problems, even on embedded devices.

Nevertheless, embedded ML presents a unique set of challenges, including the need for integration of specialized technologies, hardware resource requirements and performance considerations, and the need for skill sets that are often not present in existing conventional development teams.

Siemens' expertise in platform technology and professional services enables customers to get started quickly and easily with ML and allows them to focus on how ML can add value to their products, without first having to solve a bewildering and time-consuming array of complex platform-level challenges.

Machine Learning

In recent years, advances in hardware technology and software techniques have made it possible and practical for machine learning (ML) to provide better solutions to many real-world problems, even on embedded devices.

Nevertheless, embedded ML presents a unique set of challenges, including the need for integration of specialized technologies, hardware resource requirements and performance considerations, and the need for skill sets that are often not present in existing conventional development teams.

Siemens' expertise in platform technology and professional services enables customers to get started quickly and easily with ML and allows them to focus on how ML can add value to their products, without first having to solve a bewildering and time-consuming array of complex platform-level challenges.