User Management

Policy Management

Third Party Accounts

IAM Docs

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The vault to explore all the features  of Scalifi Ai’s 
Identity and Access Management.

Identity and Access Management

Documentation Hub

Model Designs

Model Jobs

Model Building Docs

The vault to explore all the features  of Scalifi Ai’s 
Model Building Service.

Model Building

Usage

Quota

BUC Docs

The vault to explore all the features  of Scalifi Ai’s 
Billing and Usage Center.

Billing and Usage

Model Management

Metadata

Model Version

Model Catalog Docs

The vault to explore all the features of Scalifi Ai’s Model Catalog.

Model Catalog

In Scalifi Ai's Model Catalog Service (MCS), metadata enriches model management by attaching additional, relevant information to models or model variations. MCS supports two primary types of metadata: Simple Type and Storage Type. Each type caters to specific needs and accommodates different use cases.

Types of metadata

Simple Type Metadata is designed to store basic information in key-value pairs where the value is a primitive data type. This metadata type is optimal for labelling models with straightforward data that aids in categorization, search, and brief descriptions.

Simple Type Metadata

Storage Type Metadata allows for the attachment of files and complex data structures, providing a robust means to include supplementary materials essential to the model. This metadata type supports various file formats, enhancing the model's utility with rich, accessible content.

Storage Type Metadata

To add metadata, click on Upload metadata under Metadata.

Scalifi Ai supports String, Integer, Floating point and file data type.

To add String, Integer or Floating point type metadata:

Add Metadata

To instantly preview the metadata, enable Instant file preview on click from the toolbar and click on the metadata to preview its properties.

Click on Preview action.

To view/hide the content, click on View/Hide content button.

To download the metadata, click on Download file in properties section or right click on metadata and then click on Download Metadata option.

Preview metadata

Click on Update metadata action.

Change data type as per the need and enter the updated value.

Update metadata

Click on Delete metadata action.

Delete metadata

Metadata encapsulates crucial information about a machine learning model, including hyperparameters, training data details, and evaluation metrics. This concise summary enhances model understanding, reproducibility, and collaborative efficiency.

Allowed operations that users can perform on the platform are:

But before moving on to the operations of metadata lets see what are the different types of metadata.

Metadata of models and model versions can be accessed from their respective metadata browser.