The seed funding round was led by early-stage venture capital firm Blume Ventures, with participation from Log X Ventures and Sprout Venture Partners
Funds from this round would be used to build out Scribble Data’s product roadmap, setting up a stronger presence in the North American market and strengthening its integration with third-party data solutions
Using Redis' in-memory vector similarity lookup, Enrich achieved a ~30x speedup on entity match generation
Bengaluru-based data engineering startup Scribble Data has raised $2.2 Mn in seed funding led by early-stage venture capital firm Blume Ventures. Log X Ventures and Sprout Venture Partners also participated in this round of funding.
The funds from this round would be used to build out Scribble Data’s product roadmap, which includes a low code consumption interface for teams to access and use features produced on the platform, along with additional apps that bring data teams closer to specific solutions such as anti-money laundering, benchmarking, personalization, and more.
With a growing customer base, the startup has plans to use the funds in setting up a stronger presence in the North American market. Scribble Data would also use the new capital towards strengthening its integration with third-party data solutions like Redis.
Cofounded by Venkata Pingali and Indrayudh Ghoshal in 2017, Scribble Data helps organisations take their ML models from lab to production quickly and reliably, by focusing on feature engineering.
The startup’s modular feature store, Enrich, which comprises a number of pre-built feature engineering apps, helps data teams cut time-to-market for each data science use case including unified metrics, customer behavioural modelling and recommendations.
Having collaborated with Scribble Data’s Engineering team, Redis has already deployed its high-performance entity matching solution for a joint customer where Enrich handled integration with multiple data sources, data prep pipelining and orchestration, along with batch and streaming feature engineering.
“Using Redis’ in-memory vector similarity lookup, Enrich achieved a ~30x speedup on entity match generation,” said Taimur Rashid, Chief Business Development Officer for Redis. “This opens more ways for developers and organisations to use Redis’s capabilities for machine learning use cases.”
Scribble Data had raised an undisclosed amount of funding in its first funding round in May 2020 to help scale its Enrich product in international markets.
The growth of data science platforms is fuelled by the ever-pervasive need for companies to extract in-depth insights from voluminous data. The global market for data science platforms was estimated to be worth $37.9 Bn in 2019 and was projected to grow at a compound annual growth rate of 30% to reach $140 Bn by 2024, according to reports.
“With more organisations effectively becoming data companies, there is a proliferation of high quality, compliant feature sets for machine learning (ML) and Sub-ML use cases in an organization,” said Anirvan Chowdhury, VP, Blume Investment. “We particularly liked Scribble Data’s modularized Feature Store approach and an App Store with the Enrich Feature Store as a backbone to solve for end-to-end use cases”