MLOps is clearly not “just DevOps for machine learning”. Rather it needs to combine methodologies from both worlds – data science and operations. One of the challenges is performing routine R&D tasks for ML within the context of an organization with changing HW setups. Do the data scientists have their own machines? Do they need to spin up a remote VM to perform their research? Where and how to access machines for training runs once the code has been tested? In this session we will show how having tight coupling between orchestration and experiment tracking within the MLOps stack allows providing a productivity-boosting link between R&D teams and the expensive hardware available for their work.
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.