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AION User Guide
(Release 2.8.5)
Copyright Notice
The information contained in this document is the property of HCL Technologies Limited. Except as specifically authorized in writing by HCL Technologies Limited, the holder of this document shall: (1) keep all information contained herein confidential and shall protec... | ||
SELECTOR- Feature Selection: The statistical analysis takes place to identify the relevant features for model training and remove the unimportant features based on correlation & importance.
LEARNER- Model Training Hyper Parameter Tuning: This stage trains configured models and selects the best parameters based on hype... | ||
Compatibility with previous version:
Note: Note: if a previous version of AION is already installed, re-training of use cases of that version is possible in the latest version, and it is backward compatible. But, use cases created in the latest version may not run the previous version, as forward compatibility is not ... | ||
Model Container
Retrained Model
MLaC: Machine Learning as Code generates code automatically based on ML Operations performed during various ML Lifecycle. Using MLaC, expert data scientists can have better control over experimentation, optimization & deployment of ML Models.
MLaC has four unique components.
Generat... | ||
Select authorization as Basic Authorization
Username: USERNAME
Password: PASSWORD
Put the data in the Body as a raw json format.
Click send to perform prediction.
Note: For more information users can follow the user guide for MLaC by clicking on the icon on the top right corner of the pop-up window.
Federated ... | ||
Model container: The model container creates a docker image of the trained model so that user can use that image anywhere, pull the image, and perform testing. If the user wants to consume the trained model for prediction purpose in some system where AION is not there, then either the user can install it as python pack... | ||
Eg: If an user clicks on Python Package icon from the Packages tab, a pop-up window will appear to confirm the model download. Click Ok and then the model gets downloaded in the form of a WHL file which can be used for further testing and prediction.
Model Re-training
After the Model got trained go to the Home.
Fro... | ||
While uploading the dataset, data size and ram size need to be checked in two cases:
If file size > ram size, file will not be uploaded for further process. File size should not be more than 80% of ram size.
If the file size > 50% of ram size than the alert will be shown and proceed for the next step.
Structured Da... | ||
Click External Data Sources.
Select different sql databases and data warehouses to ingest data. viz.- SQLite, Microsoft SQL, PostgreSQL, MySQL, Oracle, Google BigQuery, Redshift, Snowflake, Actian.
To access data from the database, you need to provide essential credentials like Database Name, Username, Host Port, e... | ||
To see the remaining details, click the EDA button.
On clicking the EDA button, a window will pop up that enables configuring the features and the sample of data that needed to be selected for EDA.
Select features and data subsampling size for exploratory data analysis. Finally, click Next.
Data Overview: The summ... |
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