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.
Data Officer(s) need to confront complexity of modern enterprise thru multiple data models. Knobs enable interpretation of data & build logical understanding. Most importantly knobs act as levers using which data leaders can control how information is applied in various experiment and AI processes.
Use knobs to define dataset. Efficiently construct dataset that represent real word. Learn optimal policy and generaization with compressed dataset.
Add more data points, diversity, variability to build dataset that represent world. Generate new dataset to handle cold start problem or test model
Identify, mask or remove personally identifiable information from datasets. By obscuring PII information, use data for experiments and comply with regulations.
Anonymize data to protect against identify,membership and attribute disclosure. Protect the privacy as well as make data useful for getting insight.
Focus on producing content and data. Generate web experience , mobile epxerience. Using knobs optimize for presentation layer, speed, distribution, SEO
Fine tune LLM for specific task and build custom chatbot. Build intranet, knowledgebase and chatbot. Automatically retrain chatbot, digital assistant as new help article arrives.
From the blog
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.
Our product KREATE can generate web design. Web design that are built to convert
Using KREATE you can publish marketing blog with ease. See KREATE in action
Startup and enterprise who wish to build their own data prodct can hire expertise to build Data product using generative AI