Mastering Multimodal RAG & Embeddings with Amazon Nova & Bedrock

  • BeginnerLevel

  • 1500+Students Enrolled

  • 5 Hrs Duration

  • 4.8Average Rating

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About this Course

  • Delve into word embeddings, tokenization, Byte Pair Encoding (BPE), and data sampling techniques to build a strong foundation in natural language processing.
  • Learn to utilize Amazon's Titan Text Embeddings for effective text representation, enhancing the performance of AI applications.​
  • Explore the integration of various data modalities using Amazon Nova and Bedrock, enabling the development of advanced, AI-powered solutions.

Learning Outcomes

Understanding Embeddings

Learn how embeddings enhance NLP and LLM capabilities.

Exploring Multimodal RAG

Master retrieval-augmented generation with multimodal data.

Amazon Nova & Bedrock

Utilize Amazon Nova and Bedrock for AI-powered solutions.

Course Curriculum

Explore a comprehensive curriculum covering Python, machine learning models, deep learning techniques, and AI applications.

tools

  1. 1. Introduction to the course

  2. 2. Understanding word Embeddings and Tokenization

  3. 3. Implementing Byte-Pair Encoding (BPE)

  4. 4. Data sampling with a sliding window

  1. 1. Exploring Embedding model on Amazon Bedrock

  1. 1. Multimodals and Transformers for vision

  2. 2. Understanding CLIP

  3. 3. Text Generation Multimodals

  1. 1. What is RAG

  2. 2. What is Multimodal RAG

  3. 3. Building Multimodal RAG with Amazon Bedrock, Amazon Nova and LangChain

Meet the instructor

Our instructor and mentors carry years of experience in data industry

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Suman Debnath

Principal Developer Advocate

Suman Debnath is a Principal Developer Advocate for Machine Learning at AWS, with a background in storage and performance engineering. He specializes in open-source ML tools like TensorFlow, PyTorch, and Spark.

Get this Course Now

With this course you’ll get

  • 5 Hours

    Duration

  • Suman Debnath

    Instructor

  • Beginner

    Level

Certificate of completion

Earn a professional certificate upon course completion

  • Industry-Recognized Credential
  • Career Advancement Credential
  • Shareable Achievement
certificate

Frequently Asked Questions

Looking for answers to other questions?

Text embeddings are vector representations of words, phrases, or entire documents that capture their semantic meaning. They are crucial in AI applications such as search, recommendation systems, chatbots, and Retrieval-Augmented Generation (RAG), enabling models to process and compare text efficiently.

Multimodal models process and integrate multiple types of data, such as text, images, and audio, whereas traditional AI models typically focus on a single modality. These models enable applications like visual question answering (VQA), image captioning, and AI assistants capable of understanding both text and images.

Amazon Nova is a multimodal large language model (LLM) developed by AWS that can process text and images simultaneously. It is designed for applications such as AI-powered customer support, content generation, and intelligent search, leveraging embeddings and RAG for enhanced accuracy.

Yes, you will receive a certificate of completion after successfully finishing the course and assessments.

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