Deploying and Evaluating GenAI Apps Learning Badge Path
Take your GenAI applications from creation to full employment in this MongoDB learning path! Explore chunking strategies, deployment options, and more.
This learning path helps you take your GenAI application from creation to full deployment, focusing on optimizing performance and evaluating results. You'll explore chunking strategies, performance evaluation techniques, and deployment options in MongoDB for both prototyping and production stages.
We recommend completing the Building GenAI Apps Learning Badge Path before beginning this path. To earn your badge, complete the content in this learning path and then pass the short assessment at the end. You will receive an email with your official Credly badge and digital certificate within 24 hours.
Milestones
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Learning Badge Content
Chunking Data for RAG Applications
RequiredLearning Byte
| Sarah Evans25 Minutes
Learn about how to transform your data for retrieval-augmented generation (RAG) applications using different chunking strategies.Evaluating RAG Application Results
RequiredLearning Byte
| Apoorva Joshi20 Minutes
Learn how to evaluate your RAG applications for optimal performance.Deployment Options for GenAI Apps with MongoDB
RequiredLearning Byte
| Henry Weller20 Minutes
Learn how to take your generative AI application from creation to deployment with MongoDB.Best Practices for Atlas Vector Search Performance
RequiredLearning Byte
| Prakul Agarwal20 Minutes
Review top tips to get the most out of your Atlas Vector Search instance.MongoDB and GenAI Glossary
ElectiveMicrocourse
| MongoDB University15 Minutes
A guide to key terms and concepts used when building, deploying, and evaluating generative AI applications with MongoDB. -
Learning Badge Assessment
Deploying and Evaluating GenAI Apps Learning Badge Assessment
RequiredAssessment
This assessment will test your knowledge of optimizing the performance and evaluating the results of GenAI applications as well as your understanding of chunking strategies, performance evaluation techniques, and deployment options within MongoDB for both prototyping and production stages.