Q: 1
[Modeling]
An agricultural company is interested in using machine learning to detect specific types of weeds in a
100-acre grassland field. Currently, the company uses tractor-mounted cameras to capture multiple
images of the field as 10 × 10 grids. The company also has a large training dataset that consists of
annotated images of popular weed classes like broadleaf and non-broadleaf docks.
The company wants to build a weed detection model that will detect specific types of weeds and the
location of each type within the field. Once the model is ready, it will be hosted on Amazon
SageMaker endpoints. The model will perform real-time inferencing using the images captured by
the cameras.
Which approach should a Machine Learning Specialist take to obtain accurate predictions?
Options
Discussion
C
Yeah, gotta go with C. SageMaker SSD requires RecordIO for object detection, and image classification (A or D) won't return weed locations, which is needed here. Parquet format (B, D) is a distractor. Pretty sure about this but let me know if I'm off.
A is wrong, C. RecordIO is required for SageMaker SSD, and classification alone won’t give you object locations.
Why wouldn't B work here? Isn't Parquet mostly for tabular data, and SSD on SageMaker expects RecordIO?
Its B. Went for Parquet with SSD because I thought Apache Parquet might be supported for object detection tasks too.
C imo. For object detection with SageMaker SSD, RecordIO is the supported input, not Parquet, and you need bounding boxes for location data. Saw a similar question in practice sets, matches what AWS wants. Disagree?
SageMaker docs are all over the place about formats, but yeah, C here.
Pretty sure it's C for this kind of weed location problem. Object detection like SSD can pinpoint where each weed is in the image, not just the type. RecordIO format is also what SageMaker object detection expects. Not 100% but makes sense with how SageMaker pipelines work.
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