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Learn how to free-up Data Science time for more model building and less model support.
MLOps is the best practices and standards to deploy, monitor, and improve A.I. models. Students learn an overview of MLOps and how to apply best practices for your team.
Scaling Data Science - How do you enable data science teams to build and automate more models?
Increasing R.O.I. - How do you deploy more models in production to increase R.O.I. and adoption?
Performance - How do teams quantify and improve model performance?
Understand and utilize the terminology, best practices, and critical concepts for deploying, monitoring, and governing A.I. models.
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Understand and explain what machine learning engineering is and the problem it solves to your team.
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Identify and align your team to the roles of machine learning engineering.
Automate the training, deployment, and monitoring of a model.
Measure and monitor the performance of A.I. models.
Govern A.I. models to increase R.O.I. and trust.
This enables our tailored training experience. We want to make sure each class is right for you, and that you will get the most value out of completing it.
We've created a simple form below to help you get the process started...
I felt like we hit our stride and were sharing organizational context and prior experiences alongside course concepts in a way that elevated the training. I really enjoyed our discussions.
