As companies are hiring data scientists and deploying machine learning models, data science projects present their own set of unique challenges. This panel will discuss some of the challenges enterprises encounter, how companies can put end users first when deploying data science initiatives and ML models, and examples of how to overcome some of these challenges.
Hear from enterprise experts about challenges in implementing data science in enterprise settings.
Moderator: Nir Bar-Lev (ClearML)
- Anne Bauer (NY Times)
- Aditi Saluja (T-mobile)
- Raam Roch Hai (Heineken NV)
- Radha Sankaran (Verizon)
DataRobot is the leader in enterprise AI, delivering trusted AI technology and ROI enablement services to global enterprises. DataRobot’s enterprise AI platform democratizes data science with end-to-end automation for building, deploying, and managing machine learning models.
Production AI Model Management at Scale
Automate the standardized deployment, monitoring, governance, and validation of all your models to be developed in any environment. A single, production-grade environment for all your SAS, R, Machine Learning, and Regression model needs
Zorroa’s no-code ML integration platform makes process automations with machine learning APIs from GCP, AWS, and Azure accessible in under an hour. Its platform enables media technologists to stand up rapid-cycle experiments and scale their ML projects without code, data prep, or vendor lock-in.