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Reduce Infrastructure Cost by Moving Beyond MLOps

Join Lightning CTO Luca Antiga and VP of Marketing Mike Bilodeau as they discuss the causes behind the exploding cost of MLOps and how Lightning AI reduces both the cost and time needed to build AI-powered products and services.

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.

Key takeaways:

  • 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