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You’ve Been Doing MLOps All Wrong:4 Problems You Need to Fix to Drive Value

Yes, we said it, you’ve been doing MLOps all wrong. Why?

MLOps is meant to deliver AI that creates value for the business by operationalizing machine learning on an ongoing basis. This means ML model accuracy needs to be right not just when they’re trained or when they’re deployed but also every time they’re used by the business.

As the environment changes, that means the models will have to be retrained to reflect those changes. But most enterprises treat machine learning like a one-time software release.

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Maybe these scenarios sound familiar?

  • Deploying and undeploying models into productions can take weeks or even months, making your entire AI/ML program slow to respond to rapid changes in the environment
  • A lack of observability has made it difficult to track the true accuracy of your models and risk concept drift
  • Increasing the number of models in production or managing chained models doesn’t scale, requiring new headcount as more is asked of the team
  • ML models trained on-prem or in the cloud where compute is plentiful don’t work when deployed in compute-constrained edge environments
  • Your team experiences sticker shock when your cloud compute bill comes every month as a result of running compute-intense ML models

If so, you’re not alone.

As Big Data, ML, AI and MLOps are being used to make clearer real-world decisions based on data insights, it’s important to ensure that your MLOps’s mission is to deliver AI that creates value for the business by operationalizing machine learning.

Wallaroo levels the machine learning playing field:

Easy, Secure and Scalable Deployment
Launch 3X Faster
12.5X Faster Inferencing
80% Lower Server Costs