Data Science AI API Container


Bicedeep enables its Data Science AI API as Docker image.


With the API as Docker image, you can :


==> Have all power of Bicedeep Data Science AI API locally.


==> Create AI Models on your local computer without uploading your data to cloud.


==> Work without Internet connection.


==> Query your models locally.


==> Extract models and queries created.


==> Deploy your Docker image to the cloud environment you select. The API is ready to use on-line as well.


==> Start using the API directly with our Python Client


Subscribe for $1950 / month



Documentation :




Installation :


By having Bicedeep Data Science AI Docker image, your data science power will be enhanced on your local computer system regardless of the operating system.

> Download the Docker client

> Download and Unzip Bicedeep Data Science AI API Container on this page after starting the subscription.

> Load the image to docker : sudo docker load -i ./biceapidocker

> Run the container : sudo docker run -p 3000:3000 biceapidocker

> Open the URL on browser : localhost:3000

> You should be seeing the landing page of the container. Since the container contains an API, it is better to use it with an HTTP client. We are providing an example HTTP client end example usages as Python Client. We recommend to use it for the first interactions until you have confident with the API.

> Don't forget to change baseUrl in Python Client to baseUrl = "http://localhost:3000"


Multiple Model Use For Less Error:


We recommend to use multiple model and a reducer network to decrease the error value or increase accuracy.

As an example, for the HR Analytics Year At Company Prediction, we trained three models where each of them got the error rate of 1.41, 1.24, 1.23 respectively. Prediction results values of the three models on train data are used to train another network which is called the reducer network. When predictions of the three models are given to the reducer network, it decides to use networks with errors 1.23, 1.24 and achieves error rate of 1.16


Methods List:


Methods example usages can be found in the Python Client


GET : api/fileapi/GetFileList

POST : api/fileapi/UploadAFile

GET : api/fileapi/DownloadAFile

GET : api/fileapi/DownloadModelFile

GET : api/fileapi/RenameFile

GET : api/fileapi/DeleteAFile

POST : api/reportapi/RequestReport

GET : api/reportapi/GetStatus

GET : api/reportapi/GetReport

POST : api/queryapi/CreateQuery

GET : api/queryapi/GetQueryResult

POST : api/queryapi/SubmitAQueryFile

GET : api/queryapi/DownloadAQueryFile


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Subscribe for $1950 / month