The Pain. Setting up a scalable, hosted Data-driven web app is a tedious, time consuming process requiring contributions from a team of backend/infrastructure engineers, software/systems engineers and front-end engineers and product designers alongside data engineers and machine learning engineers/data scientists.

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As a result, developing data-driven products is resource intensive, time consuming and process-ridden within any organization. Subsequently, experimentation with product design is extremely limited. Therefore, the time to market for bringing innovative product design is either extremely long or never happens.

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What is needed? A low-code platform for a product person to quickly iterate with product ideas by experimenting with them in a production environment. There are solutions that tackle aspects of this problem: cloud service providers (AWS, GCP, Azure, etc.) enable access to commodity compute and data storage for backend and infrastructure engineers; platforms for machine learning and data science (TensorFlow, Databricks, etc.) provide access to machine learning infrastructure for data scientists; programmable dashboards (Dash, for example) enable building data-intensive interactive dashboards for front-end engineers; and wire-framing platforms (Figma, etc.) enable building designs for product designers. However, none of these enable a product person, on her own and without a team, to get all of these together to build a production prototype — this is urgently needed.

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The Solution. With Ikigai, one can build data-driven interactive web applications in a low-code environment. In effect, using a variety of connectors, data from first party or third party sources (including web scrape and all that) can be accessed using a low-code computation environment. It is feasible to prepare, process, analyze, predict and optimize using data in a secure environment, using a low-code dashboarding environment. It is also possible to bring a wire-framed design to life that is intimately connected with computational tasks to enable interactivity. Finally, such dashboards can be published as a hosted web app to bring a web application to life; and indeed, it can be easily integrated into an existing web app stack through appropriate URL redirection to enable experimentation.

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To explain this, let us take an example of building a movie recommendation system using live interaction data. The process will be divided into three steps. One, connecting to data. Two, processing data, adding recommendation algorithm and enabling interaction. Three, building a dashboard that interacts with the earlier steps to both enrich data, compute recommendations and provide an interactive experience to the end user. Finally, we can publish the dashboard as a web app.

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Step 1. Connecting to a data source in Ikigai is as simple as dragging and dropping our Imported Facet! In this case, we point it to the MovieLens dataset.

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Step 2. Here we show the final pipeline after connecting the remaining facets. It consists of using our Recommend Facet to produce collaborative-filtering based recommendations and the remaining facets provide metadata [Movie Title, Similarity] for our predictions to enhance output interpretability.

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Step 3. Finally, we create the D2i-App powered by the flow shown in the prior step that allows users to have an interactive experience with the data!

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This final app can be shared easily with stakeholders within/outside the organization with the click of a button, using anything else would be — as Spock would put it “Highly Illogical”. Ready to get started today? Book a demo with our team here.

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About the Author

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Arth Dharaskar (Applied Data Scientist)

Arth Dharaskar is an Applied Data Scientist at Ikigai Labs where he is working on building a scalable machine learning infrastructure. He holds a Master’s degree from the University of California — San Diego specializing in Machine Learning and Data Science and a Bachelor’s degree in Computer Science from Rutgers University. When he is not working, you will find him lifting weights or reading books!

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Originally posted on medium.com