ADA is a six-step process, the first four of which are part of the implementation, during which Sogeti's ADA experts will work to train and implement an AI model, and the final two of which will assist end users in requesting data.
Figure: ADA six-step process
Step 1 – Extract a real dataset – ADA requires high-quality "production-alike" data in order to train the AI model on data characteristics. This step will extract and clean up the data.
Step 2 – Ingest to ADA – The extracted data will be ingested into the ADA framework, which will then be used to learn about the data. Various accelerators will be used to pre-process the data and make it trainable during this stage. Additionally, this step will be used to create a mapping of the data's referential integrity.
The term "referential integrity" refers to the reliance and relationship of data across databases, tables, and applications.
Step 3 – Scrub or Mask – This is an optional step in which the data is scrubbed or masked if it is too sensitive to train with in its current state.
Step 4 – Train ADA – This stage utilizes the mapping and training set data to train an ADA framework model. The model gains knowledge of the data's characteristics and relationships during this stage by iteratively traversing the data. This stage results in a trained model that is completely knowledgeable about the data (the data type, format, rules, nature of data, relationships etc.).
Step 5 – Generate Data – At this stage, an interface will be used to connect to the train model, and end users will be able to request data by providing the model with a few simple parameters. The model will generate the data and perform the necessary operations to store it.
Step 6 – Push data – This step is used to push data to a database or shared drive, or to an API or other mode of data transfer. This step is completely customizable to the client's specifications.
The trained model can be deployed both in the cloud and on-premises. The entire technology stack is open source and requires no licenses.