LLM Cost Calculation Framework | Slides

Enterprises are most successful when they treat data like a product. It enable to use data in multiple use cases. However data product should be designed differently compared to software product.

Generative AI has enabled many transformative scenarios. We combine generative AI, AI, automation, web scraping, ingesting dataset to build new data products. We have expertise in generative AI, but for business benefit we define our goal to build data product in data centric manner.

To build a commercial data product, create a base data product. Then add extension to these data product by adding various types of transformation. However it lead to complexity as you have to manage Data Lineage. Use knobs for lineage and extensibility

As per HBR " Data product require validation of both 1. whether algorithm work 2. whether user like it". Builders of data product need to balance between investing in data-building and experimenting. Our product KREATE focus on building dataset and apps , ABExperiment focus on ab testing. both are designed to meet data product development lifecycle

In complex problems you have to run hundreds of experiments. Plurality of method require in machine learning is extremely high. With Dataknobs approach, you can experiment thru knobs.

See Drivetrain appproach for building data product, AI product. It has 4 steps and levers are key to success. Knobs are abstract mechanism on input that you can control.