Today, according to McKinsey, 85% of ML models fail, leading to loss of time, revenue and business impact. Understanding common risk factors for model failure has a direct impact on improving the visibility and predictability of ML development cycles, especially for ADAS & AV applications. In this session, we will reveal the criteria executives can use to accelerate their ML models’ path to production — and keep them there. We will share best practices such as how to:

• Improve predictability of ADAS & AV algorithms
• Add transparency to the process of understanding when and why models fail
• Achieve faster iteration times for ML models
• Lower the Total Cost of Ownership (TCO) of your ML programs