In this course we’ll join Dr. Denis Batalov, worldwide AI/ML Tech Leader, as he shows you how to implement a machine learning pipeline using Amazon SageMaker and Amazon SageMaker Ground Truth. First you will create a labeled dataset, then you’ll create a training job to train your object detection model, and finally you will use Amazon SageMaker to create and update your model.
Intended Audience
This course is intended for:
- Developers and data scientists who want to create machine learning pipelines with Amazon SageMaker using the Sagemaker SDK and python.
- Developers and data scientists who want to use Amazon SageMaker Ground Truth to create their own labeled datasets.
Course Objectives
In this course, you will learn how to:
- Train a machine learning model using images labeled by Amazon SageMaker Ground Truth
- Use Amazon SageMaker Ground Truth to identify the exact location of bees on individual images in a dataset
- Train the object detection model using Amazon SageMaker in-built algorithms
- Use an automated hyperparameter tuning job to find an optimal set of hyperparameters
Prerequisites
We recommend that attendees of this course have the following prerequisites:
- A basic understanding Amazon SageMaker (https://aws.amazon.com/sagemaker/)
- A basic understanding of the python programming language with various libraries like Pandas, NumPy, SageMaker, and Boto3
Delivery Method
This course is delivered through:
- Digital training
Duration
- 70 minutes
Course Outline
This course covers the following concepts:
- Downloading data
- Running a labeling job
- Training a model
- Deploying a model
- Hyperparameters/automated model tuning
- Examining hyperparameter optimization results
- Replacing a machine learning production model
