ModelArts Samples
This document provides ModelArts samples concerning a variety of scenarios and AI engines to help you quickly understand the process and operations of using ModelArts for AI development.
ExeML Samples (Basic)
|
Sample |
Function |
Scenario |
Description |
|---|---|---|---|
|
ExeML |
Object detection |
Based on the Yunbao dataset, use the object detection algorithm of ModelArts ExeML to identify Yunbao in images. |
|
|
ExeML |
Predictive analytics |
Predict whether customers would be interested in a time deposit based on their characteristics, including the age, work type, marital status, education background, housing loan, and personal loan. |
Built-in Algorithm Samples (Basic)
|
Sample |
Engine |
Function |
Scenario |
Description |
|---|---|---|---|---|
|
TensorFlow |
Training Management > Built-in Algorithms |
Image classification |
Use the built-in ResNet_v1_50 algorithm to train the flower data to recognize flower types. |
Notebook Samples (Basic)
|
Sample |
Engine |
Function |
Scenario |
Description |
|---|---|---|---|---|
|
TensorFlow |
|
Image recognition |
Develop training scripts based on the MoXing framework and achieve the recognition of handwritten digits based on the AI development lifecycle. |
Common Framework Samples (Basic for AI Development Lifecycle)
|
Sample |
Engine |
Function |
Scenario |
Description |
|---|---|---|---|---|
|
MXNet |
|
Image recognition |
Develop training scripts based on the MXNet engine and achieve the recognition of handwritten digits based on the AI development lifecycle. |
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|
TensorFlow |
|
Image recognition |
Develop training scripts based on the TensorFlow engine and achieve the recognition of handwritten digits based on the AI development lifecycle. |
|
|
Caffe |
|
Image recognition |
Develop training scripts based on the Caffe engine and achieve the recognition of handwritten digits based on the AI development lifecycle. |
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|
Spark MLlib |
|
Prediction and recommendation |
Use the Spark MLlib algorithm to obtain the direction of precision marketing and assist decision-making, increasing the conversion rate of consumer goods and profits of merchants as well as improving consumer experience. |
|
|
MXNet |
|
Image recognition |
Based on the MXNet engine, train the Caltech dataset to achieve the recognition of Caltech images. |
|
|
Spark MLlib |
|
K-nearest-neighbor (kNN) classification |
Use the k-Nearest Neighbor (kNN) classification algorithm for vehicle satisfaction survey. The Car Evaluation dataset is used to evaluate six features to obtain the satisfaction of users on vehicles. |
|
|
Spark MLlib |
|
Classification and prediction |
Use the four attributes (sepal length, sepal width, petal length, and petal width) to predict which type (Setosa, Versicolour, and Virginica) an iris flower belongs to. |
Ascend Application Samples (Basic)
This document provides the following samples for algorithms that support Ascend applications. You can use the built-in algorithms (from Training Management or AI Market) of ModelArts to support your service applications by referring to the following samples.
|
Sample |
Engine |
Function |
Scenario |
Description |
|---|---|---|---|---|
|
Using a Built-in Algorithm for Object Detection (Ascend 310) |
TensorFlow |
Built-in algorithms (in Training Management) and Ascend 310 inference |
Object detection |
Use built-in algorithms to train a model, and use Ascend 310 to deploy the model as a real-time service. |
Application Samples of Advanced Functions (Advanced)
|
Sample |
Engine |
Function |
Scenario |
Description |
|---|---|---|---|---|
|
MoXing |
Notebook |
Image classification |
Use the MoXing framework on ModelArts to identify icebergs and ships in images. |
|
|
MoXing |
|
Image recognition |
Develop training scripts based on the MoXing framework and achieve the recognition of handwritten digits based on the AI development lifecycle. |
Next Article: ExeML
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