AWS – Deep Learning with Sagemaker

Categories AWS

About Course

Deep learning with AWS SageMaker focuses on - Data Processing thru AWS Data Wrangler - Autopilot - models with MXNET and TF/Keras - Deployment options - Foundation models with JUMPSTART models

Course Curriculum

Introduction & recap

Overview of the course

00:00
AWS priors

00:00
Machine Learning – recap

00:00
Deep learning – recap

00:00

Introduction to TF/Keras

Introduction to Apache MXNet

AWS Storage

Amazon Simple Storage Service (Amazon S3) – overview

00:00
Object Storage, Buckets, Objects, Object Keys and Metadata

00:00
S3 – Mgmt console

00:00
S3 – Python code

00:00
S3 – Benefits

00:00
Data Consistency:

00:00
Access Control:

00:00
Amazon Elastic Block Store (Amazon EBS)

00:00
Storage Classes

00:00
Data Lifecycle Management

00:00
Data Transfer

00:00
Monitoring and Logging

00:00

AWS Data Wrangler

Sagemaker – vanilla models

Sage Maker – AutoPilot

Hyperparameter opt – Automatic Model Tuning

Model Deployment

Model Monitoring and Scaling

Monitoring Metrics

00:00
Data Drift Detection

00:00
Concept Drift Detection

00:00
Model Performance Thresholds

00:00
Data Bias Monitoring:

00:00
Anomaly Detection

00:00
Data Quality Monitoring

00:00
Automated Model Retraining

00:00
Alerting and Reporting

00:00
Model Explainability

00:00

AWS Deep Learning AMIs

Sagemaker – Jumpstart models

Amazon Elastic Inference

Distributed Training

GPU Instances

Cost Optimization

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Free

Free access this course

Level

Intermediate
Duration

24 hours
Last Updated

July 29, 2023

AWS – Deep Learning with Sagemaker

About Course

What I will learn?

Course Curriculum

Introduction & recap

Overview of the course

AWS priors

Machine Learning – recap

Deep learning – recap

Introduction to TF/Keras

Introduction to Apache MXNet

The motivation for and benefits of using MXNet and Gluon

Important terms and APIs used in MXNet

Convolutional neural networks (CNN) architecture

Hands-on lab: Training a CNN on a CIFAR-10 dataset

AWS Storage

Amazon Simple Storage Service (Amazon S3) – overview

Object Storage, Buckets, Objects, Object Keys and Metadata

S3 – Mgmt console

S3 – Python code

S3 – Benefits

Data Consistency:

Access Control:

Amazon Elastic Block Store (Amazon EBS)

Storage Classes

Data Lifecycle Management

Data Transfer

Monitoring and Logging

AWS Data Wrangler

Data Loading and Extraction:

Data Transformation and Preparation

Data Partitioning and Bucketing

Data Compression and Encryption

Data Writing and Loading

Data Quality and Validation

Integration with AWS Services:

Error Handling and Logging

Sagemaker – vanilla models

Sage maker – Overview

Sage Maker – ML env

Hands-on lab: Spinning up an Amazon SageMaker notebook instance and running a multilayer perceptron neural network model

Sage Maker – AutoPilot

Overview – Auto Pilot

tabular data – churn pred

tabular data – cal housing

image classification

text classification

from experiment to production

Summarize (autopilot)

Hyperparameter opt – Automatic Model Tuning

Model Tuning with SageMaker

Define metrics and environment variables

Define Hyperparameter Ranges

Track and set completion criteria for your tuning job

Tune Multiple Algorithms with Hyperparameter Optimization to Find the Best Model

Example: Hyperparameter Tuning Job

Model Deployment

Deployment – process

Various deployment options & right choice

Real-Time Inference

Serverless Inference

Batch Transform

Asynchronous Inference

Model Monitoring and Scaling

Monitoring Metrics

Data Drift Detection

Concept Drift Detection

Model Performance Thresholds

Data Bias Monitoring:

Anomaly Detection

Data Quality Monitoring

Automated Model Retraining

Alerting and Reporting

Model Explainability

AWS Deep Learning AMIs

AMIs and DLAMIs

Deep learning AMIs – what are they

How DLAMI works

Developer Features

Selecting the Instance Type for DLAMI