Building Multimodal Search and RAG

DeepLearning.AI is a specialist online education platform founded in 2017 that focuses on practical, industry-relevant training in artificial intelligence and machine learning. Designed for learners at multiple levels, its courses emphasize hands-on projects, clear conceptual foundations and tools commonly used in industry. The platform’s flexible online format makes it accessible to professionals and students worldwide who want to build skills without relocating.

Courses and specializations are structured to help learners develop applied portfolios, with real-world assignments, code notebooks and community review. Collaboration with leading practitioners ensures content remains current with industry practice, and certificate programs help demonstrate competencies to employers. The platform also supports career transitions, offering guidance on interviewing for technical roles and connecting learners with opportunities to showcase their work.

For international learners seeking concentrated, practice-oriented AI education, DeepLearning.AI provides a clear route to upskill quickly and build demonstrable expertise. Its global learner community, modular course design and emphasis on project-based learning make it a pragmatic choice for those aiming to enter research, product or engineering roles in the AI ecosystem.

This non-degree program is for students who want to learn about building multimodal search and RAG systems. It's a great fit for those who want to develop skills in AI and machine learning. The program is offered in English and can be completed in a few weeks.

The curriculum covers topics like contrastive learning, multimodal models, and visual instruction tuning. Students will learn how to build any-to-any multimodal search and implement an end-to-end multimodal RAG system. They will also explore industry applications and create a multi-vector recommender system.

After completing this program, students can pursue careers as AI developers, data scientists, or machine learning engineers. They can work in industries like tech, healthcare, or finance, and companies like Google, Microsoft, or Amazon. Specific job titles may include AI researcher, natural language processing specialist, or computer vision engineer.