Data Science is hard, achieving ROI from your AI projects is even harder. Data Scientists spend more time wrangling data and slinging models to software and devops engineers than time developing and analyzing their ML models. The solution is to enable a culture shift similar to the DevOps movement where developers manage software quality in production - data scientists should manage ML model performance in production environments. Dedicated ML Engineers are helping to bridge this transition, but they struggle with the tools and automations required to enable scale with autonomy.

Join Manish Modh, Founder & CEO of Andromeda 360 AI on this journey to envision a world of autonomous data science and how Data Scientists and ML Engineers are empowered to own the development, deployment, operations, and performance of their machine learning use cases. Experience the challenges data science teams face today and why most AI projects fail. Learn the art of the possible that leverages all of the wisdom gathered over 20 years of technology evolution from Big Data, Cloud, DevSecOps, AI/ML, and Edge computing.