Build, Train, and Deploy Machine Learning Models with Amazon SageMaker
In this course, you are going to learn the skills you need to build, train, and deploy machine learning models in Amazon SageMaker, including how to create REST APIs to integrate them into your applications for solving real-world problems.
What you'll learn
A fully managed machine learning service is a great place to start if you want to quickly get machine learning into your applications. In this course, Build, Train, and Deploy Machine Learning Models with Amazon SageMaker, you will gain the ability to create machine learning models in Amazon SageMaker and to integrate them into your applications. First, you’ll learn the basics and how to set up SageMaker. Next, you’ll discover how to build, train, and deploy models applied to Image Classification for breast cancer detection and how to integrate them into a REST API. Finally, you will even discover how to manage security and scalability in Amazon SageMaker. When you’re finished with this course, you will have a foundational understanding of Amazon SageMaker that will help you immensely as you move forward to create your own machine-learning-enabled applications applied to different real-life scenarios.
Table of contents
- Introduction 1m
- SageMaker Notebook Instances 1m
- Creating a Notebook Instance 3m
- Overview of the Image Classification Built-in Algorithm 3m
- Obtaining, Exploring, and Preprocessing Histopathology Images 10m
- Configuring the Image Classification Algorithm Using the Low-level AWS SDK for Python 8m
- Configuring the Image Classification Algorithm Using the High-level SageMaker Python Library 2m
- Overview of Using Tensorflow in SageMaker 2m
- Converting Images to the TFRecord Format 4m
- Configuring a Tensorflow Estimator Using the High-level SageMaker Python Library 9m
- Overview of Using Apache MXNet in SageMaker 2m
- Configuring a MXNet Estimator Using the High-level SageMaker Python Library 10m
- Summary 1m
- Introduction 1m
- Overview of Creating Training Jobs in SageMaker 3m
- Creating and Monitoring a Training Job for the Built-in Image Classification Algorithm Using the Low-level AWS SDK for Python 6m
- Creating and Monitoring a Training Job for the Built-in Image Classification Algorithm Using the High-level SageMaker Python Library 4m
- Creating and Monitoring a Training Job for the Custom Tensorflow Algorithm Using the High-level SageMaker Python Library 4m
- Creating and Monitoring a Training Job for the Custom MXnet Algorithm Using the High-level SageMaker Python Library 4m
- Overview of Automatic Hyperparameter Optimization 2m
- Creating and Monitoring a Tuning Job for the Built-in Image Classification Algorithm Using the Low-level AWS SDK for Python 9m
- Creating and Monitoring a Tuning Job for the Built-in Image Classification Algorithm Using the High-level SageMaker Python Library 3m
- Creating and Monitoring a Tuning Job for the Custom Tensorflow Algorithm Using the High-level SageMaker Python Library 4m
- Creating and Monitoring a Tuning Job for the Custom MXnet Algorithm Using the High-level SageMaker Python Library 3m
- Summary 1m
- Introduction 1m
- Overview of Deploying and Testing Machine Learning Models in AWS SageMaker Hosting Services 1m
- Deploying and Testing the Trained Model Based on the Built-in Image Classification Algorithm Using the Low-level AWS SDK for Python 7m
- Deploying and Testing the Trained Model Based on the Built-in Image Classification Algorithm Using the High-level SageMaker Python Library 4m
- Deploying and Testing the Trained Model Based on a Custom Tensorflow Algorithm Using the High-level SageMaker Python Library 4m
- Deploying and Testing the Trained Model Based on a Custom Mxnet Algorithm Using the High-level SageMaker Python Library 4m
- Overview of Integrating Endpoints with AWS API Gateway and AWS Lambda 2m
- Integrating an AWS SageMaker Endpoint with AWS API Gateway and AWS Lambda 6m
- Summary 1m
- Introduction 1m
- Overview of Managing Authentication and Access Control Using IAM Policies 2m
- Configuring Access Control to Notebook Instances 8m
- Overview of Monitoring and Troubleshooting Deployed Models with AWS CloudWatch 2m
- Analyzing Endpoint Metrics and Logs with AWS CloudWatch 2m
- Overview of Configuring Automatic Scaling for AWS SageMaker Endpoints 1m
- Configuring Automatic Scaling for an AWS SageMaker Endpoint Using the AWS Console 2m
- Summary 1m
About the author
Jorge is a passionate person who loves building quality software that allows people to solve their problems. He also loves to teach, that's why he works several years ago teaching programming and software development to university students. He has experience developing highly performant backend systems with Java and Node.js, building ETL processes with Python and Scala and working with cloud platforms such as Amazon Web Services. His current areas of interest include Deep Neural Networks, Comput... more
