


DS2.ai is the MLOps platform that serves all processes of building your customized AI from automatic annotation to cloud deployment.
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Data Annotation
- Supports 5 types of labeling tools such as object detection and tabular data
- Unlimited auto annotation function
AI training
- Magic Code: Customizable auto-generated AI training code
- AutoML : Automatic creation of over 100 AI models after hyperparameter tuning
- Automatic App Creation using AI model
Model Deployment
- Cloud Type: One click AI model deployment to the cloud service of your choice
- Server Type : Inference acceleration functions contained One click deployment to the backend server
Operation
- Custom Training: Multi-GPU clustering supported Jupyter server setting
- GPU monitoring support
- support a various of inference system : Video forcast, image prediction, recommend system, abnormal detection, etc.
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Upload data
Data Auto annotation
Automated Deep learning
AI model deployment
Automatic App Creation
Saving inference results
Data retraining
Improve model accuracy
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Finance
Insurance
Manufacture
Logistic
Marketing
Business
Farm
Energy
Law
Public
Others
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AI solutions without the MLOps system are not able to utilize the continuously generated data so the AI performance is not expected to improve.
With the MLOps system, you can quickly utilize the new data and improve artificial intelligence by correcting the model with the continuously generated data.
* Manufacturing has been described as an example, and MLOps can be applied to all industries where AI can be introduced.
DS2.ai supports all processes required in developing Customized AI. It is an AI-specialized platform service that collects data, builds a training dataset through data labeling, and enables the automatic development of artificial intelligence and easy deployment and operation.
Build a data pipeline for MLOps at DS2.ai with Python. SDK gives you access to all of the processes from uploading and labeling data, to training and deploying the artificial intelligence model.
from ds2 import DS2
ds2 = DS2(apptoken=“s2234k3b4”)
ds2.predict(
"people.jpg",
quick_model_name = “person”
#model_id=20000 # Or You can also use your custmized AI.
)
{
"images": [
{
"id": "60a212aac869a1fea276480d",
"file_name": "/images/img_labelingExample.jpg",
"width": 4000,
"height": 2084
}
],
"type": "instances",
"annotations": [
{
"segmentation": [
[
1200,
907,
1200,
1882,
2903,
1882,
2903,
907
]
],
"area": 1660425,
"iscrowd": 0,
"ignore": 0,
"image_id": "60a212aac869a1fea276480d",
"bbox": [
1200,
907,
1703,
975
],
"category_id": 2621,
"id": "60a216ae2cd9eb1bbde44e2b"
}
],
"categories": [
{
"supercategory": "none",
"id": 2620,
"name": "person"
},
{
"supercategory": "none",
"id": 2621,
"name": "person"
},
{
"supercategory": "none",
"id": 2622,
"name": "person"
}
]
}