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AWS SageMaker Complete Course| PyTorch & Tensorflow NLP-2023

Name: AWS SageMaker Complete Course| PyTorch & Tensorflow NLP-2023
Rating: 3.6 (130 reviews)

Build DL/ML model in Sklearn,Tensorflow/Keras & PyTorch. How to bring docker container&Algorithm from local to Sagemaker

Created byData Analytics Platform Academy(DAP)

Last updated 8/2023

English

What you'll learn

What is SageMaker and Why it is required
SageMaker Architechure
Model Building using existing Docker Image in SageMaker
Model Building using existing algorithm in SageMaker
Model Building using SageMaker Pre-built algorithms
Model Building in Tensorflow/Keras
Model Building in Pytorch
How to deploy the models in SageMaker
How to make predictions from Endpoints
Create complete End-to End machine learning Pipeline Workflow
Real time example of NLP
How to schedule the SageMaker notebook for Retraining
How to Build ,deploy and schedule the Model
AWS Certified Machine Learning Specialty (MLS-C01)

Course content

8 sections • 41 lectures • 5h 28m total length

AWS SageMaker Course Summary4:48
SageMaker Introduction2:46
SageMaker Benefits1:59
SageMaker Architecture5:42
SageMaker Notebook Instance Creation10:07

Bring your Docker container-Part17:53
bring a custom docker container from your local machine, push it to SageMaker, and train and deploy a transformer-based classification model with input, model, output folders, and an endpoint.
Bring your Docker container-Part212:20
Bring your Docker container-Part313:48
Create and deploy a Docker image and container by writing a Dockerfile, selecting a Ubuntu base, installing Python and libraries, and running a training script to evaluate model performance.
Bring your Docker container-Part45:01
Bring your Docker container-Part59:57
Bring your Docker container-Part67:09
Bring your Docker container-Trouble Shooting Part72:27
Bring your Docker container-Part85:00

Bring your BERT Docker container-Part11:17
Bring your BERT Docker container-Part221:50
Bring your BERT Docker container-Part311:45
Bring your BERT Docker container-Part43:31
Bring your BERT docker container, part 4, to demonstrate preparing a BERT-based pipeline, installing transformers and vision dependencies, handling image inputs, and building a notebook workflow.
Bring your BERT Docker container-Part55:27
Bring your BERT Docker container-Part66:55
Bring your BERT Docker container-Part75:02

BYOA-Part17:51
Learn to containerize a custom SageMaker algorithm by encapsulating code in functions, configuring a Dockerfile, defining an entry point, and organizing input, configuration, model, and output folders.
BYOA-Part215:33
BYOA-Part37:05
BYOA-Part43:48
BYOA-Part56:17
Build a random forest model to predict heart disease using features such as blood pressure, cholesterol, and gender, with a 70/30 train-test split.
BYOA-Part628:01
BYOA-Part78:05

SageMaker Pre-built Algorithm: Linear Regression-Part17:06
SageMaker Pre-built Algorithm: Linear Regression-Part22:54
SageMaker Pre-built Algorithm: Linear Regression-Part313:56
Explore the SageMaker pre-built linear regression workflow, from dataset preparation and exploratory analysis to train test splits and training with S3 storage.
SageMaker Pre-built Algorithm: Linear Regression-Part410:18
SageMaker Pre-built Algorithm: Linear Regression-Part512:09

Create Pipeline, Workflow and schedule the SageMaker Notebook-Part13:18
Create Pipeline, Workflow and schedule the SageMaker Notebook-Part26:48
Create an AWS step function pipeline that ties data preparation, training, and deployment in SageMaker, and learn to set up a notebook and IAM roles for scheduling.
Create Pipeline, Workflow and schedule the SageMaker Notebook-Part310:13
Create Pipeline, Workflow and schedule the SageMaker Notebook-Part412:27
Create a SageMaker training pipeline and workflow by integrating training, model, and transform steps into a single workflow, deploy endpoint, and schedule the step function with cloud watch.
Create Pipeline, Workflow and schedule the SageMaker Notebook-Part56:24
Schedule a SageMaker notebook pipeline with CloudWatch events and a step function to train, transform data, and deploy an endpoint in an automated ML lifecycle.

Requirements

Free or paid subscription to AWS is required. It may ask for Phone and/or Credit Card for verification
Python Basic knowledge

Description

This course is complete guide of AWS SageMaker wherein student will learn how to build, deploy SageMaker models by brining on-premises docker container and integrate it to SageMaker. Course will also do deep drive on how to bring your own algorithms in AWS SageMaker Environment. Course will also explain how to use pre-built optimized SageMaker Algorithm.

Course will also do deep drive how to create pipeline and workflow so model could be retrained and scheduled automatically.

This course covers all aspect of AWS Certified Machine Learning Specialty (MLS-C01)

This course will give you fair ideas of how to build Transformer framework in Keras for multi class classification use cases. Another way of solving multi class classification by using pre-trained model like Bert .

Both the Deep learning model later encapsulated in Docker in local machine and then later push back to AWS ECR repository.

This course offers:

AWS Certified Machine Learning Specialty (MLS-C01)

What is SageMaker and why it is required

SageMaker Machine Learning lifecycle

SageMaker Architecture

SageMaker training techniques:

Bring your own docker container from on premise to SageMaker

Bring your own algorithms from local machine to SageMaker

SageMaker Pre built Algorithm

SageMaker Pipeline development

Schedule the SageMaker Training notebook

More than 5 hour course are provided which helps beginners to excel in SageMaker and will be well versed with build, train and deploy the models in SageMaker

Who this course is for:

Data Engineers or data scientist
Developers who want to start a career in or wants to learn about the exciting domain of Data Science and Machine Learning
Business Analysts who want to apply Data Science to solve business problems
Learn how to build train and deploy it in AWS cloud

AWS SageMaker Complete Course| PyTorch & Tensorflow NLP-2023

What you'll learn

Explore related topics

Course content

Amazon SageMaker: Introduction5 lectures • 25min

Bring your own (Any) Tensorflow/Keras Docker container to SageMaker8 lectures • 1hr 4min

Bring your own (Any) PyTorch BERT Docker Container7 lectures • 56min

Bring Your own(Any) Algorithm in SageMaker7 lectures • 1hr 17min

SageMaker Pre-built Algorithm5 lectures • 46min

SageMaker Pre-built Algorithm: LogisticRegression2 lectures • 14min

Model Deploy and Making Predictions2 lectures • 8min

Create Pipeline, Workflow and schedule the SageMaker Notebook5 lectures • 39min

Requirements

Description

Who this course is for: