Challenges Transitioning Machine Learning from Prototyping to Production Grade

The artificial intelligence revolution is being enabled by the rapid increase in data, computing power in the cloud, and hardware accelerators like GPUs and algorithm innovations. However, only < 5% of organizations have adopted or deployed AI in their operations and > 80% are gathering knowledge and prototyping. In this webinar, we identify the challenges of transitioning from Prototype to Production. We break them down into four categories of challenges: 1) Code portability, 2) Scalability, 3) Hyperparameter optimization or Automated machine learning, and 4) Model Deployment. In this talk, we will address challenges 1 & 2 using RocketML to aid in building production-grade AI applications. These techniques will not only improve data scientists' productivity, but will also reduce total cost of ownership (TCO) compared to market incumbents. The webinar is hosted by Fundoo.info and moderated by IITAGH Board Member.