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MLOps Bootcamp: Build Real-World AI Infrastructure
Introduction
Introduction (2:06)
Exercise: Meet Your Classmates and Instructor
What We Are Building (12:17)
Demo: How The Retrain Pipeline Gets Kicked Off (5:11)
Course Resources
Understanding Your Video Player
Set Your Learning Streak Goal
Understanding AWS, IAM roles, Domains and Notebooks
Signing in to AWS (3:11)
Understanding IAM Roles and Permissions (6:33)
Setting Up SageMaker Domain (1:34)
Understanding SageMaker Notebooks (10:29)
Let's Have Some Fun (+ More Resources)
Installing Required Packages and Dependencies to our Sagemaker Notebook
Installing Sagemaker Versions (9:20)
Importing Libraries and Tools (13:12)
Important Reminder to Shut Down Your AWS Server (1:19)
Unlimited Updates
Accessing our Dataset and doing Exploratory Data Analysis
Downloading the Data From HuggingFace (6:47)
Addressing Rate Limit Errors (3:04)
Exploratory Data Analysis (4:17)
Uploading Dataset to S3 (5:45)
Verifying the Dataset in S3 (3:21)
Implement a New Life System
Setting up our AWS Sagemaker Training Job with Vision Transformers
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
Starting the Training Job on a GPU Server (4:40)
Debugging Training Errors (3:05)
Evaluating the Training Job (3:05)
Course Check-In
Deploying our Model to Sagemaker and Setting up Monitoring Dashboards
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
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
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
Triggering and Testing the Retrain Pipeline - Part 1 (6:39)
Triggering and Testing the Retrain Pipeline - Part 2 (4:56)
Triggering and Testing the Retrain Pipeline - Part 3 (8:44)
Where To Go From Here?
Thank You! (1:17)
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Creating Main Train Function in the Train Script
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