Launch app in AWS Sagemaker

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Section 1: Introduction to Sagemaker

  1. Introduction to Sagemaker
  2. Download Resources
  3. Sagemaker Features
  4. Intro to the simple end-to-end ML problem
  5. Free Tier Options

Section 2: Sagemaker Studio

  1. Sagemaker Studio Setup (Live)
  2. Exploring the Studio
  3. How sagemaker works internally

Section 3: Sagemaker Initial Setup

  1. Setting up Boto and sagemaker packages
  2. Execution Role and role of S3

Section 4: Getting Data into Sagemaker and S3

  1. Begin data preprocessing
  2. How to upload data from S3 from Sagemaker?

Section 5: Preprocessing Jobs

  1. How to handle compute intensive tasks in dedicated container
  2. Overview of Docker
  3. Preprocessing job - part 1
  4. Preprocessing job - part 2
  5. Why transfer the Python code file to S3
  6. Understanding the arguments for running the processing job
  7. Inspecting Processing Jobs

Section 6: Sagemaker Experiments

  1. Experiments and Trials
  2. Steps to conduct an experiment in sagemaker
  3. Creating tracker
  4. Create Trial Image
  5. Running training trial for XGBoost Estimator

Section 7: Model Deployment

  1. Batch processing vs model serving
  2. Real Time Inference
  3. Model Endpoint Part 1 - Creating Model.mp4
  4. Model Endpoint Pt 2 - Create Endpoint Config
  5. Model Endpoint Pt 3 - Create Endpoint and monitor
  6. Remember to delete endpoints

Section 8: Hyper Parameter Tuning in Sagemaker

  1. Hyper Parameter Tuning with Sagemaker
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