There are three built-in SageMaker image processing algorithms. They are all Supervised Learning algorithms and so have to be trained using labelled data. Each one analyzes images in a different way and returns different inference data for downstream processing. SageMaker’s three built-in image processing algorithms each have their own way of visualizing real word objects. The question you want answered will determine which algorithm you use.
These revision notes are part of subdomain 3.2 Select the appropriate model(s) for a given machine learning problem of the exam syllabus.
Image Processing Articles
via Gfycat The Semantic Segmentation algorithm processes images by tagging every single pixel in the image. This fine grained approach enables the information about the shapes of objects and edges to be gathered. A common use case is computer vision. The output of training is a Segmentation Mask which is a RGB or grayscale PNG…
The SageMaker Image Classification algorithm can apply multiple labels to an image depending on what objects are identified. Objects are either identified, or not, there are no probability scores. Attributes Problem attribute Description Data types and format Image Learning paradigm or domain Image Processing, Supervised Problem type Image and multi-label classification Use case examples Label/tag…
The SageMaker Object Detection algorithm identifies and classifies objects in images. The identified object is placed in a class with a numerical measure of confidence. The location in the image is identified by a bounding box around the object. Object Detection is a Supervised Learning algorithm trained on a corpus of labeled images. Because the…
Whizlab’s AWS Certified Machine Learning Specialty practice exams
Whizlab’s AWS Certified Machine Learning Specialty Practice tests are designed by experts to simulate the real exam scenario. The questions are based on the exam syllabus outlined by official documentation. These practice tests are provided to the candidates to gain more confidence in exam preparation and self-evaluate them against the exam content.
Practice test content
- Free Practice test – 15 questions
- Practice test 1 – 65 questions
- Practice test 2 – 65 questions
- Practice test 3 – 65 questions
Section test content
- Core ML Concepts – 10 questions
- Data Engineering – 11 questions
- Exploratory Data Analysis – 13 questions
- Modeling – 15 questions
- Machine Learning Implementation and Operations – 12 questions