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Build, Train, and Deploy a Machine Learning Model with Amazon Sagemaker
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# codemental #machinelearning #sagemaker
I found that the tutorial is slightly out of date, so you will find that I had to update the code to get it to use the updated Sagemaker APIs
FOLLOW ME
SUPPORT THE CHANNEL or get MENTORING :
TIMESTAMPS
0:00 Introduction
3:00 Step 1 - Accessing the Amazon Sagemaker console
3:35 Step 2 - Create an Amazon Sagemaker notebook instance
4:54 Step 3 - Prepare the data
5:53 Selecting a Sagemaker Endpoint for training
6:20 Creating an S3 bucket to store the data for training
9:43 Step 4 - Train the model from the data
10:00 Understanding XGBoost algorithm
17:58 Resolving a compile error - train_instance_count has been renamed in Sagemaker
18:51 Resolving error CreateTrainingJob operation(reached maxretries: 4) Rate Exceeded
20:45 Step 5 - Deploying the Model
RESOURCES
I found that the tutorial is slightly out of date, so you will find that I had to update the code to get it to use the updated Sagemaker APIs
FOLLOW ME
SUPPORT THE CHANNEL or get MENTORING :
TIMESTAMPS
0:00 Introduction
3:00 Step 1 - Accessing the Amazon Sagemaker console
3:35 Step 2 - Create an Amazon Sagemaker notebook instance
4:54 Step 3 - Prepare the data
5:53 Selecting a Sagemaker Endpoint for training
6:20 Creating an S3 bucket to store the data for training
9:43 Step 4 - Train the model from the data
10:00 Understanding XGBoost algorithm
17:58 Resolving a compile error - train_instance_count has been renamed in Sagemaker
18:51 Resolving error CreateTrainingJob operation(reached maxretries: 4) Rate Exceeded
20:45 Step 5 - Deploying the Model
RESOURCES