Autoplay
Autocomplete
Previous Lesson
Complete and Continue
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)
Review This Course!
Become An Alumni
Learning Guideline
ZTM Events Every Month
LinkedIn Endorsements
Creating The Endpoint Update Lambda Function for the SageMaker Pipeline
This lecture is available exclusively for ZTM Academy members.
If you're already a member,
you'll need to login
.
Join ZTM To Unlock All Lectures