ML in Production

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Learn to select optimal deployment and monitoring patterns, optimize model performance, and address production challenges across various data types while enhancing label consistency.

Key AI Functions:Concept Drift,ML Deployment Challenges,Human-level Performance (HLP),Project Scoping and Design,Model baseline

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Description for ML in Production

Features of Course

Select the optimal deployment and monitoring patterns for various production scenarios by identifying the critical components of the ML project lifecycle and pipeline.

Optimize the performance and metrics of the model by prioritizing disproportionately essential examples that represent key slices of a dataset.

Address the production challenges associated with structured, unstructured, small, and large data, as well as the importance of label consistency and the ways in which it can be enhanced.

Level: Intermediate

Certification Degree: Yes

Languages the Course is Available: 22

Offered by: On Coursera provided by DeepLearning.AI

Duration: 3 weeks at 3 hours a week

Schedule: Flexible

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