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Mammoth Club All levels 1 sections 8 lectures

Exam 10 - Databricks Certified Generative AI Engineer Associate

Test your knowledge (with 45+ questions and unlimited attempts!) on Evaluation and Monitoring: Select an LLM choice (size and architecture) based on a set of quantitative evaluation metrics, select key metrics to monitor for a specific LLM deployment scenario, evaluate model performance in a RAG application using MLflow, use inference logging to assess deployed RAG application performance, use Databricks features to control LLM costs for RAG applications, use inference tables and Agent Monitoring to track a live LLM endpoint, identify evaluation judges that require ground truth, compare the evaluation and monitoring phases of the Gen AI application life cycle.

01
Skill level
All levels
02
Sections
1
03
Lectures
8
04
Instructor
Alex Kropf
What's inside

This course includes.

1
Sections
8
Lectures
8
Quizzes
Certificate of completion
Included
Mobile and desktop access
Included
AI learning assistance
Included
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Course content

Curriculum & lectures.

1 sections · 8 lectures
+ Exam 8 lectures
Select an LLM choice (size and architecture) based on a set of quantitative evaluation metrics Locked
Select key metrics to monitor for a specific LLM deployment scenario Locked
Evaluate model performance in a RAG application using MLflow Locked
Use inference logging to assess deployed RAG application performance Locked
Use Databricks features to control LLM costs for RAG applications Locked
Use inference tables and Agent Monitoring to track a live LLM endpoint Locked
Identify evaluation judges that require ground truth Locked
Compare the evaluation and monitoring phases of the Gen AI application life cycle Locked
Instructors

Taught by people who ship.

Alex Kropf

Alex Kropf

Instructor

Alex Kropf is Mammoth Club's CLO, public speaker, consultant, IT author and Senior Software Developer. Alex has produced 1,000+ best-selling courses, books and workshops for Mammoth Club, Course Pro and our clients since 2016.

Ready to start building?

Test your knowledge (with 45+ questions and unlimited attempts!) on Evaluation and Monitoring: Select an LLM choice (size and architecture) based on a set of quantitative evaluation metrics, select key metrics to monitor for a specific LLM deployment scenario, evaluate model performance in a RAG application using MLflow, use inference logging to assess deployed RAG application performance, use Databricks features to control LLM costs for RAG applications, use inference tables and Agent Monitoring to track a live LLM endpoint, identify evaluation judges that require ground truth, compare the evaluation and monitoring phases of the Gen AI application life cycle.

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