What can AutoML do?
AutoML is a complex technology requiring a deep understanding of the core concepts behind Machine Learning and automation. The article provides visualizations of the supported automation flow and details typical use cases covered with the AutoML technology.
Cognitive Automation
AutoML allows you to automate at scale and deliver continuously to production without a Data Scientist in a team. Here's a quick visualization of the Cognitive Automation flow.
The diagram below explains the overall Cognitive Automation workflow step by step:
Typical use cases
IA Cloud is used for, but not limited to, the following use cases:
- Information Extraction is a process of extracting structured information (or key facts) from unstructured or semi-structured documents.
- Classification is the arrangement of data into groups or categories according to established criteria. Both the binary and multi-class types are supported.
Information Extraction
Information Extraction (IE) is when data defined by business logic is taken out (extracted) from documents and processed according to business rules. In terms of Information Extraction, each data point is referred to as a field.
For example, for invoices, Information Extraction covers such data points as invoice number, supplier name, and quantity of products. It means there are three fields to be extracted from documents—invoice number, supplier name, and quantity.
For more information about IE models, refer to the Information Extraction topic.
Classification
Classification is applied when it is necessary to define the class for an item (document). Classes stand for different document types.
For example, one business flow can include invoices, purchase orders, and claims, but each document type has to be handled differently. This means, before applying automation, you need to classify these documents.
For more information about Classification models, refer to the Classification topic.
More automation opportunities
The following video covers typical use cases automated in Insurance, Healthcare, Financial Services, and other sectors. It describes addressed problems and how they are solved using the platform and the outcomes.
For more in-depth information, refer to Learn ML basics.