ML Ops: Challenges of operating ML models at scale

ML Ops: Challenges of operating ML models at scale Data science is becoming a mature field with new challenges. We are well past the time when a proof of concept or prototype was sufficient to demonstrate value. ML models need to be productized and deployed, and when we are dealing with hundreds of models in […]

ML Ops: Challenges of operating ML models at scale

Data science is becoming a mature field with new challenges. We are well past the time when a proof of concept or prototype was sufficient to demonstrate value. ML models need to be productized and deployed, and when we are dealing with hundreds of models in real-time simultaneously, the only way to succeed is to apply ML Ops concepts. However, data science has its particularities that make this process more difficult and complex.

The aim of this masterclass is to provide a simple introduction to understand the challenges and become familiar with the kind of solutions that need to be put in place.

 

Speaker: 

Felipe Calderero

 
 
 

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ML Ops: Challenges of operating ML models at scale

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ML Ops: Challenges of operating ML models at scale
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