Example Curriculum
Introduction
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Understanding AWS, IAM roles, Domains and Notebooks
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Installing Required Packages and Dependencies to our Sagemaker Notebook
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Accessing our Dataset and doing Exploratory Data Analysis
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Setting up our AWS Sagemaker Training Job with Vision Transformers
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- Configuring Our Vision Transformer Train Setup (9:00)
- Creating Helper Functions and Evaluations for the Train Script (14:13)
- Creating Main Train Function in the Train Script (8:06)
- Setting Up Model Configurations for Training (9:12)
- Getting Metrics During Training (6:38)
- Saving Model Artifacts (2:54)
- Setting Up SageMaker Estimator (8:09)
Training our Vision Transformer on Nvidia GPUs in AWS
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Deploying our Model to Sagemaker and Setting up Monitoring Dashboards
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- Creating the Inference Script - Part 1 (15:58)
- Creating the Inference Script - Part 2 (7:19)
- Creating the Inference Script - Part 3 (9:29)
- Deploying a Live Endpoint (3:48)
- Testing our Endpoint and our Data Capture Configuration (3:57)
- Setting Up CloudWatch Dashboards for Monitoring our Pipeline (9:54)
- Shutting Down the Notebook Server (1:03)
Creating our Sagemaker Retrain Pipeline
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- Understanding the Re-Training Pipeline (9:05)
- Creating The Endpoint Update Lambda Function for the SageMaker Pipeline (15:47)
- Endpoint Configuration for SageMaker Pipeline and A_B Test Setup (18:18)
- Finishing The Lambda Deploy Model Function (7:05)
- Comparing Model Metrics in the Re-Train Pipeline (9:23)
- Getting the Candidate Model_s Metrics (15:29)
- Brief Explanation on the Previous Video (3:07)
- Compare Metrics Function (10:03)
- Saving Comparison Metrics to the SageMaker Directory (9:32)
- Importing Modules for the SageMaker Pipeline (11:08)
- Creating Pipeline Definitions (13:51)
- Setting Up the Processing Steps in the SageMaker Pipeline (11:12)
- Adding The Comparison Step to the SageMaker Pipeline (6:56)
- Adding the Conditional Lambda Deployment to the SageMaker Pipeline (13:55)
- Clarification on Condition Step (2:07)
- Exercise: Imposter Syndrome (2:55)
Connecting the Sagemaker Pipeline with other AWS Services
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- Understanding The Lambda Function that Connects API Gateway with our SageMaker Pipeline (8:34)
- Setting Up The Connection Between API Gateway, SageMaker Endpoint, and the Pipeline - Part 1 (19:29)
- Setting Up The Connection Between API Gateway, SageMaker Endpoint, and the Pipeline - Part 2 (13:50)
- Clarification Reagrding the List Pending Examples Function (6:25)
- Setting Up The Connection Between API Gateway, SageMaker Endpoint, and the Pipeline - Part 3 (14:17)
- Setting Up The Connection Between API Gateway, SageMaker Endpoint, and the Pipeline - Part 4 (14:19)
- Setting Up The Connection Between API Gateway, SageMaker Endpoint, and the Pipeline - Part 5 (4:16)
- Updating Our Train Function to Have the Retrain Method (14:35)
- Setting Up API Gateway and the Lambda Integration (6:16)
- Importing Pipeline Definition File to Our SageMaker Notebook (4:47)
- Creating our SageMaker Pipeline and Visualizing the Graph_udemy_fixed (3:19)
- Setting Up Deployment Notebook for Professional Deployment (15:43)
- Testing API Gateway and Our CloudWatch Dashboard_udemy_fixed (5:24)
- Checking the Retraining Pipeline - Part 1 (8:00)
- Updating the SageMaker Pipeline with the Appropriate GPU Server (5:53)
- Updating Lambda Function with Appropriate Permissions (1:28)
Testing our Final Sagemaker Pipeline
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Where To Go From Here?
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