AI Workbench

Maturing the engineering aspects of AI solution development, deployment and management

AI Solutions are radically different from traditional applications

  1. Traditional applications were rules & requirements driven and are built to deliver repeatable and precise answers. However, as both AI algorithms and data continue to evolve, managing nuances such as explainability, repeatability & uncertainty of results is challenging.

  2. Unlike traditional applications, incremental additions to requirements for AI Solutions do not necessarily translated to incremental efforts. Seemingly adjacent problems may have wildly differing solutions e.g., The algorithm that can transcribe text cannot extract summary from text.

These challenges require an innovative approach to manage the lifecycle of AI Solutions

AI Solutions need different building blocks to manage their lifecycle

Across various phases of the AI Solution lifecycle - From Problem definition to Solution development, deployment & maintenance - evolving AI algorithms, exploding data, computational advances, data security need to be addressed to serve enterprise needs.

Aganitha’s AI workbench addresses these aspects through a comprehensive set of tools that achieve the following. Read more about how AI workbench addresses key aspects that are of interest to the management, devops and developers of an enterprise in our detailed whitepaper.

For management:

  1. Systemic understanding of the existing models and their limitations.
  2. Explore the existing data in the enterprise in conjunction with relevant external data from public databases etc.
  3. Provide simple POCs to understand the limitations and applications of the AI solutions.

For devops:

  1. Upgrade the models and reproduce the results on demand.
  2. Manage the intermediate data as necessary, including the features computed as a part of the system.
  3. Understand the relationships between data and the models and features.

For developers:

  1. Reduce coding while being able Reuse and Repurpose existing code.
  2. Understand the current state of the art so that the solution does not attempt to solve fundamental issues.
  3. Manage complex workflows among AI solutions - a feature flow perspective of the activities.