Many enterprises today face numerous challenges around handling data for AI/ML. They find themselves having to manually extract datasets from a variety of sources, which wastes time and resources. Data scientists need to request access to the data from the data engineer or data owner, repeating this process every time a new model needs to be built. This often results in the use of data that is not the most up-to-date, loss of accuracy, and a lengthy process of getting data science to production.
But this doesn’t have to be the case. Today, solutions exist to unify data from multiple sources, tap into these sources directly and continuously, and automating feature generation, model training, ensemble learning, and monitoring of models in production and the triggering of retraining as needed. These solutions include AutoML tools to simplify the process even further.
In this session, we discussed end-to-end automation of the production pipeline and how to govern AI in an automated way. We’ll touch upon setting up a feedback loop, generating explainable AI, and doing all of this — at scale.
Watch this session to hear about:
- Automatically and continuously tapping into multiple historic and real-time data sources to run AI/ML
- Transferring your AI models from training to production at scale on Azure cloud or in hybrid environments
- Governing your AI applications with automated processes and generating explainable AI
- Improving model accuracy using a feature store and model monitoring capabilities