Description

Introduction

Natural Language Processing (NLP) on AWS SageMaker is designed to help learners build, train, and deploy NLP models at scale using SageMaker’s managed services. From text classification and sentiment analysis to named entity recognition and language modeling, this course demonstrates how SageMaker supports both built-in algorithms and custom deep learning frameworks like Hugging Face, TensorFlow, and PyTorch for advanced NLP use cases.

Prerequisites

Participants should have:

• A basic understanding of machine learning concepts.

• Experience with Python and popular NLP libraries (e.g., NLTK, spaCy, Hugging Face Transformers).

• Familiarity with SageMaker environments and the AWS ecosystem.

• Some exposure to text data and preprocessing techniques.

Table of Contents

1. Introduction to NLP and AWS SageMaker
    1.1 What is NLP?
    1.2 Common NLP Tasks and Use Cases
    1.3 NLP Capabilities in SageMaker

2. Preparing Text Data for Modeling
    2.1 Text Cleaning and Normalization
    2.2 Tokenization and Stopword Removal
    2.3 Vectorization: TF-IDF, Word2Vec, BERT Embeddings
    2.4 Creating Input Data in SageMaker Format

3. Text Classification with Built-in Algorithms
    3.1 Using BlazingText for Classification
    3.2 Training, Evaluation, and Inference
    3.3 Visualizing and Interpreting Results

4. Sentiment Analysis and Named Entity Recognition
    4.1 Custom Models with Hugging Face Transformers
    4.2 Fine-Tuning Pretrained Models
    4.3 Deploying NLP Models Using SageMaker Inference

5. Working with Pretrained NLP Models
    5.1 Using SageMaker Prebuilt Hugging Face Containers
    5.2 Zero-Shot and Few-Shot Learning
    5.3 Performance Optimization for Large Language Models

6. Advanced NLP Use Cases
    6.1 Text Summarization and Question Answering
    6.2 Language Translation with Sequence-to-Sequence Models
    6.3 Chatbots and Conversational AI with SageMaker

7. Model Deployment and Monitoring
    7.1 Real-time Inference and Batch Transform Jobs
    7.2 Monitoring NLP Endpoints and Logging
    7.3 Auto-scaling and Cost Optimization

8. Automation and Pipelines
    8.1 Automating Text Data Ingestion and Processing
    8.2 Building NLP Pipelines with SageMaker Pipelines
    8.3 Integration with EventBridge, Lambda, and S3

9. Real-World NLP Project
    9.1 Analyzing Customer Reviews (Classification & Sentiment)
    9.2 Data Preprocessing, Model Training, and Deployment
    9.3 Dashboarding and Business Integration

Natural Language Processing on SageMaker opens up powerful opportunities to extract insights from text at scale. With native support for popular frameworks and seamless deployment options, SageMaker simplifies every step of the NLP workflow—from preprocessing to prediction. This course gives you the tools and confidence to develop robust, production-ready NLP solutions on AWS.