Machine Learning - Training Custom Models
Machine learning is the process of teaching a computer system how to make predictions through experience. In order for a computer to understand a model must be created on an initial set of data. This lab is aimed at machine learning beginners who want to understand how to train custom models. After completing this lab you will understand how to create a machine learning model based on a categorized set of images. Additionally, you will gain familiarity with Amazon Rekognition, and basic Python concepts for interacting with the sample model.
Upon completion of this lab you will be able to:
- Understand machine learning model training over a set of images concepts
- Utilizing Python to interact with the Amazon Rekognition Models to classify images
This lab is intended for:
- Individuals starting out with machine learning
- Anyone interested in training custom models and/or image classification
You should possess:
- A basic understanding of Python
April 18th, 2023 - Updated instruction to address the Jupyter interface experience
January 13th, 2023 - Added instructions to address the Jupyter interface experience.
December 21st, 2021 - Added instructions to reflect the latest Jupyter interface experience.
June 16th, 2020 - Added instructions on how to clean up the Rekognition model and project.
Calculated Systems was founded by experts in Hadoop, Google Cloud and AWS. Calculated Systems enables code-free capture, mapping and transformation of data in the cloud based on Apache NiFi, an open source project originally developed within the NSA. Calculated Systems accelerates time to market for new innovations while maintaining data integrity. With cloud automation tools, deep industry expertise, and experience productionalizing workloads development cycles are cut down to a fraction of their normal time. The ability to quickly develop large scale data ingestion and processing decreases the risk companies face in long development cycles. Calculated Systems is one of the industry leaders in Big Data transformation and education of these complex technologies.