Amazon SageMaker Examples
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Introduction

  • Introduction to Amazon SageMaker

Get started on SageMaker

  • Customer Churn Prediction with XGBoost

Prepare data

  • Amazon SageMaker Data Wrangler
  • Distributed Data Processing using Apache Spark and SageMaker Processing
  • Get started with SageMaker Processing

Train and tune models

  • Hyperparameter Tuning with the SageMaker TensorFlow Container
  • Train a SKLearn Model using Script Mode

Deploy models

  • Host a Pretrained Model on SageMaker
  • Deploying pre-trained PyTorch vision models with Amazon SageMaker Neo
  • Use SageMaker Batch Transform for PyTorch Batch Inference

Track, monitor, and explain models

  • Amazon SageMaker Multi-hop Lineage Queries
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Orchestrate workflows

  • Orchestrate Jobs to Train and Evaluate Models with Amazon SageMaker Pipelines
  • SageMaker Pipelines Lambda Step

Popular frameworks

  • Regression with Amazon SageMaker XGBoost algorithm
  • Hugging Face Sentiment Classification
  • Iris Training and Prediction with Sagemaker Scikit-learn
  • MNIST Training with MXNet and Gluon
  • Train an MNIST model with TensorFlow
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SageMaker Studio

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  • Framework examples
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Introduction to Amazon Algorithms

  • Introduction to Amazon Algorithms

SageMaker End-to-End Examples

  • Fraud Detection System
  • Music Recommender
  • Understanding Trends in Company Valuation with NLP
    • Understanding Trends in Company Valuation with NLP

Patterns

  • The ML Gateway Pattern

SageMaker Use Cases

  • Music Streaming Service: Customer Churn Detection
  • Fleet Predictive Maintenance
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  • Computer Vision for Medical Imaging
  • Pipelines with NLP for Product Rating Prediction
  • Credit Risk
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  • Feature selection
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Ingest Data

  • Get started with data ingestion
  • Athena
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Label Data

  • Ground Truth

Prep Data

  • Get started with data prep
  • Detect pre-training data bias
  • Image data guide
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Feature Store

  • Get started with SageMaker Feature Store

Frameworks

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  • Apache MXNet
  • Deep Graph Library
  • PyTorch
  • R
  • Scikit-learn
  • TensorFlow
  • JAX

Training

  • Algorithms
  • Reinforcement Learning
  • Debugger
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  • Distributed Training
  • Training Compiler
  • Script Mode
  • Bring Your Own Container
  • Management Features
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Inference

  • Deploy Models with SageMaker
  • Batch transform
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  • Data types
  • Model Compilation with Neo
  • Model deployment
  • Model monitor
  • Multi-Model Deployment
  • Nvidia Triton Inference
  • Model Governance
  • Shadow Testing

Workflows

  • Get started with SageMaker Pipelines
  • Pipeline Parameterization
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  • Pipeline Compare
  • Launch AutoML with Pipelines
  • Processing
  • Spark
  • Step Functions
  • Notebook Jobs

Advanced Functionality

  • Advanced Functionality
  • Serverless Inference

Advanced examples

  • SageMaker Clarify
  • Science of ML
  • AWS Marketplace
  • Amazon Sagemaker Geospatial Service

Community examples

  • Contributions
Amazon SageMaker Examples
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  • Understanding Trends in Company Valuation with NLP
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Understanding Trends in Company Valuation with NLP

  • Understanding Trends in Company Valuation with NLP
    • Introduction
      • Orchestrating company earnings trend analysis, using SEC filings, news sentiment with the Hugging Face transformers, and Amazon SageMaker Pipelines
      • Using SageMaker Pipelines
      • Understanding trends in company valuation (or similar) with NLP
      • SEC Dataset
      • News articles related to the stock symbol – dataset
    • MLOps for NLP using SageMaker Pipelines
    • Create a Custom Container
      • 1. Grant appropriate permissions to SageMaker
      • 2. Build a custom Docker image
    • Set Up SageMaker Project
      • Install and import packages
      • Define parameters that you’ll use throughout the notebook
      • Define parameters to parametrize Pipeline Execution
    • Preparing SEC dataset
      • Install the SageMaker JumpStart Industry SDK
      • Obtain SEC data using the SageMaker JumpStart Industry SDK
    • Set Up Your MLOps NLP Pipeline with SageMaker Pipelines
      • Step 1: Data pre-processing - extract SEC data and news about the company
      • Step 2: Create models for summarization and sentiment analysis
      • Step 3: Register model
      • Step 4: Deploy model
      • Step 5: Summarize SEC report step
      • Step 6: Sentiment inference step - SEC summary and news articles
    • View Evaluation Results
    • Clean up
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