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Blog posts

Generative AI in Pension Risk Transfer: Introduction, and Key Use Cases

Warren Buffett famously noted that ‘someone’s sitting in the shade today because someone planted a tree a long time ago.’ Pension risk transfer, or PRT, did not just pop up overnight. It’s got history. Think of it as a response to a big problem: companies promising pensions they later find tough to keep. This dilemma […]

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OpenAI’s Custom GPTs: Future Impact and Considerations

The automobile factory was nothing before the assembly line. It was slow. Men built one car at a time. Then the assembly line started, and it was never the same. It went fast. It was a car, then another car, and they came off the end of the line one after the other. This historic […]

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Generative AI in Insurance: Use Cases and Future Impact

What if the devastating Hurricane Katrina or Cyclone Nargis had been anticipated with greater precision, its impact mitigated by proactive insurance protocols? How would the landscape of life and health insurance change if underwriters could accurately simulate and understand the long-term health trends of populations? And what if reinsurers could preemptively navigate market collapses or […]

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Generative AI in Insurance: Introduction and Key Trends

Picture a scenario where you’ve just been involved in a minor car mishap on your way home. The usual protocol would involve a lengthy wait for an insurance adjuster’s inspection and assessment. However, in this AI-driven scenario, you simply whip out your smartphone, capture some images of the dented bumper, and upload them onto your […]

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Sparse Expert Models: A complete guide

Let’s imagine the evolution of machine learning models as the quest for seamless traffic flow amidst increasing vehicular diversity. Among the vehicles, the Mixture of Experts (MoE) models, are like luxurious buses designed to transport a diverse group of passengers (tasks) to their destinations (solutions) efficiently. Each passenger can find a seat (expert network) tailored […]

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Lightweight AI: Techniques, Applications, and Key Trends

Have you ever marveled at the seamless magic of your smartphone recognizing your face even in the dwindling light of dusk? Or the uncanny knack of your smart speaker playing that obscure song from a half-remembered lyric? Behind these marvels lies a rapidly evolving field—Lightweight AI. It’s a world where machine learning models shed their […]

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Generative AI: A Technical Deep Dive into Security and Privacy Concerns

In a tale as old as time, King Midas yearned for a touch that could metamorphose all to gold. His wish was granted, and the world around him shimmered with the allure of endless wealth. Every object he grazed turned to gold, dazzling yet cold to the touch. The ecstasy of boundless power was intoxicating, […]

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Navigating Bias and Fairness Challenges in AI/ML Development

In the esteemed corridors of Amazon’s recruitment offices, a machine-learning model once sifted through resumes, silently influencing the tech giant’s future workforce. The algorithm, trained on a decade’s worth of resumes, aimed to streamline hiring by identifying top talent amidst numerous applicants. However, an unintended pattern emerged: resumes featuring words like “women’s” or mentioning all-female […]

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The Future of Data Product Development header image

The Future of Data Product Development: Exploring Key Trends

The year is 2023, and Sarah, a data analyst at a leading tech firm, no longer spends hours writing complex SQL queries or sifting through vast datasets. Instead, she simply asks her data product, powered by a Large Language Model (LLM), “What were the sales trends last quarter?” and receives a comprehensive, human-like response. This […]

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Case Studies

Finding Conversion Anomalies at a Large E-Commerce Firm

Learn how a leading multi-billion dollar e-commerce company used Enrich to identify anomalies in their conversion rates and to find out their causal factors.

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Terrapay case study with Scribble Data

Streamlined data insights and agile data preparation for Terrapay

Learn how Terrapay, a leading cross-border payment infrastructure solution provider built the Terrapay Intelligence Platform (TIP) with Enrich to achieve operational efficiency through use cases such as Forecasting, Partner Performance Analytics, Customer Journey Analytics, and more.

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Mars great resignation case study with Scribble Data

How Mars Took Steps to Evaluate the Potential Impact of the “Great Resignation”

Learn how Mars, a Fortune 100 CPG company collaborated with Scribble Data to assign a “probability of attrition” through data, and ML modeling.

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Accelerated ML Engineering for a Leading E-Commerce Brand

Learn how a leading e-commerce brand selling children’s apparel built their data intelligence platform on Scribble Data that supported the rapid development and deployment of use cases such as Product Listing Optimization and Re-ordering.

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Shopping paths at a mall chain with Scribble Enrich

Understanding Shopping Paths at a National Mall Chain

Learn how a nationwide mall chain used Scribble Data’s Enrich platform to identify patterns of shopper footfalls, determine the timing and location of ads, and achieve a significant M-o-M increase in revenue.

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Data lake enrichment for a national retail chain using Scribble Enrich

A National Level Retail Store Chain

A national level retail chain in India leverages Scribble Data Enrich for developing an accurate understanding of their buyer personas, their distribution, demand and context at a fine granularity to address multiple operational use cases.

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Lifecycle of a Data Product with Dr. Venkata Pingali

Watch this session where Dr. Venkata Pingali, Founder & CEO of Scribble data shares his perspective on Data Products, the types of Data Products, and the lifecycle of Data Products with the Data Heroes community.

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Customer Testimonial: Cloudphysician

Dileep Raman, Cloudphysician’s Co-founder and Chief of Healthcare, talks about how Scribble Data enabled them to rapidly build pipelines to transform their data and get daily updated feature sets as well as trustworthy models – all in less than 4 weeks!

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Customer Testimonial: Mars, Inc.

Dr. Vidyotham Reddi of Mars, Incorporated–a leading US-based multinational CPG manufacturer of confectionery, pet food, and other food products and a provider of animal care services, talks about his experience of working with Scribble Data. Learn how his team at Mars was able to assign a “probability of attrition” to employees, calculated based on which […]

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What’s the deal with sentient AI? – Achint Thomas

Sentience in AI has always been the holy grail for computer science. What qualifies as AI sentience, and what is just another case of a model mimicking the data it’s trained on?

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Anatomy of a production ML feature engineering platform – Venkata Pingali

This talk draws upon the Scribble’s experience in building and evolving a production feature engineering platform, and the many conversations we have had with user data scientists. The talk will focus on the learnings, and not on the Scribble product itself, and expand on the talk from Fifth Elephant Mumbai in Jan 2019 on reducing […]

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Accelerating ML using Production Feature Engineering Platform by Venkata Pingali

Anecdotally, only 2% of the models developed are productionized, i.e., used day to day to improve business outcomes. Part of the reason is the high cost and complexity of productionization of models. It is estimated to be anywhere from 40 to 80% of the overall work.

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Global Feature Store Meetup #13 – Scribble Data

Feature stores have been traditionally designed for complex ML applications (Big-ML) that normally assume clear and high value propositions, long lead times, skilled staff, and advanced methods. Sub-ML is a space of mid-complexity ML applications where there is higher uncertainty in terms of value, methods used, available staffing, and speed is critical. Sub-ML is interesting […]

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Operationalizing responsible Machine Learning

ML models have to be both economically viable and FAccT (Fair, Accountable, Transparent). The terminology is new but not the need to defend models or to attest they can be trusted. Such requirements were present from the 70s for credit scoring models. What has changed is the scale and scope.

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Experimentation in Data Science

The ‘science’ in Data Science refers to the process of developing systematic understanding of the world through observations and experimentation. This science is happening in the context of fast moving organizations, in near realtime, and by folks who have varied backgrounds. The most familiar version of the experimentation is the A/B testing.

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Webinar Post

Scribble Conversations: Responsible AI at the Enterprise

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