Upon accessing the project, your initial task is to upload pertinent data sets. These may encompass data utilized for training, testing, validation, production, or any other data integral to your project's scope and requirements.
Data upload
To upload data, we need to pass the file path and Tag.
To upload data to the project, you have the option to either directly provide a file path or pass a Pandas DataFrame.
If you are uploading data for the first time, it's necessary to configure the project details in the 'Project config'. This config will be used for all further Operations and cannot be changed once set.
You can achieve this and upload data through our SDK by utilizing the following commands:
To upload the data into the project. This will also build the initial ML model.
Data can be uploaded to the project either directly with file or by passing Pandas DataFrame.
Data upload through data connectors:
You can upload data using various data connectors, including S3, GCS, Google Drive, SFTP, and Dropbox. The availability of these connectors will depend on your subscription plan.
S3:
Create data connector
GCS
Create data connector
Google Drive
Create data connector
SFTP
Create data connector
Dropbox
Create data connector
To view all linked data connectors for current project:
Once the data connectors are created, you can test the connect using the below function:
Test connection
List buckets
List File paths
Upload Data
Upload Feature Mapping
Upload Data description
Additional functions:
To see uploaded model info.:
Once the data is uploaded, you can also view the files, and file info through SDK.
Some additional functions:
Once uploaded you can see your Project Config. Check feature exclude and include and match with your setting.
Additionally, AryaXAI AutoML framework may choose to remove additional columns if the missing values are greater than 30%. You can override this in the AutoML model settings and retrain the model
Once the initial data configuration is completed, you can upload additional data sets, such as testing, validation data, or you can add with your own tag without needing to reconfigure the settings.
Additionally, you can also delete the uploaded file:
To fetch all tags which user has uploaded
Upon accessing the project, your initial task is to upload pertinent data sets. These may encompass data utilized for training, testing, validation, production, or any other data integral to your project's scope and requirements.
Data upload
To upload data, we need to pass the file path and Tag.
To upload data to the project, you have the option to either directly provide a file path or pass a Pandas DataFrame.
If you are uploading data for the first time, it's necessary to configure the project details in the 'Project config'. This config will be used for all further Operations and cannot be changed once set.
You can achieve this and upload data through our SDK by utilizing the following commands:
To upload the data into the project. This will also build the initial ML model.
Data can be uploaded to the project either directly with file or by passing Pandas DataFrame.
Data upload through data connectors:
You can upload data using various data connectors, including S3, GCS, Google Drive, SFTP, and Dropbox. The availability of these connectors will depend on your subscription plan.
S3:
Create data connector
GCS
Create data connector
Google Drive
Create data connector
SFTP
Create data connector
Dropbox
Create data connector
To view all linked data connectors for current project:
Once the data connectors are created, you can test the connect using the below function:
Test connection
List buckets
List File paths
Upload Data
Upload Feature Mapping
Upload Data description
Additional functions:
To see uploaded model info.:
Once the data is uploaded, you can also view the files, and file info through SDK.
Some additional functions:
Once uploaded you can see your Project Config. Check feature exclude and include and match with your setting.
Additionally, AryaXAI AutoML framework may choose to remove additional columns if the missing values are greater than 30%. You can override this in the AutoML model settings and retrain the model
Once the initial data configuration is completed, you can upload additional data sets, such as testing, validation data, or you can add with your own tag without needing to reconfigure the settings.
Additionally, you can also delete the uploaded file:
To fetch all tags which user has uploaded