About this Webinar
One of the biggest challenges facing ML practitioners and businesses today is building the complex infrastructure required to run a ML system in production. Historically, going from zero to one in machine learning required a huge amount of compute resources, cloud experts, and DevOps engineers, which limited the deployment of ML technology to companies with deep pockets. Regardless of whether you’re training models for state of the art research, building simple demos, or creating end-to-end systems for an industry use case, you need a machine learning infrastructure that can scale cost in the same way that the cloud does.
- Why platform standardization is the key to solving the fragmentation problem in ML
- How to move beyond MLOps and reduce infrastructure costs by 30-65%
- How to turn your models into scalable ML systems in days, not months