Resources

Blog posts

Buy-Ins vs Buy-Outs

Buy-Ins vs Buy-Outs in Pension Risk Transfer: A Detailed Study

Markets heave and dip like the swells of a restless ocean, unpredictable and ever-changing. Amid these swells, pension schemes are adrift, challenged by relentless waves of economic shifts and longer lives. Each year, the lives of retirees hang more precariously on decisions made not only with numbers but with nerve. In the heart of these […]

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Explainable AI: A Comprehensive Guide

Explainable AI: A Comprehensive Guide

In our world, AI has grown out of sci-fi tales into the fabric of daily life. At Harvard, scientists crafted a learning algorithm, SISH, a tool sharp as a scalpel in the vast anatomy of data. It finds diseases hidden like buried treasure, promising a new dawn in diagnostics. This self-taught machine navigates through the […]

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Role of AI and ML in Asset Management

Role of AI and ML in Asset Management: A Complete Guide

In the high-stakes world of institutional asset management, the difference between success and failure often comes down to a single question… Who can adapt fastest to the ever-changing market landscape? Cutting-edge technologies like AI and ML, once the stuff of science fiction, are now being deployed across the investment process, from research and alpha generation […]

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A comprehensive PRT guide for plan sponsors

Pension Risk Transfer for Plan Sponsors: A Complete Guide

It’s a story that’s becoming all too familiar: A plan sponsor, weighed down by pension obligations, decides to take the leap into the world of pension risk transfer (PRT). And why not? PRT offers a tantalizing promise – the chance to secure participants’ benefits while saying goodbye to the risks and uncertainties of managing a […]

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Genetic Algorithms

How Genetic Algorithms are Shaping AI and ML

Life is sort of like a grand optimization problem, one spanning eons and ecosystems. The players? Generations upon generations of organisms, each carrying an encoded blueprint – their genes – that shape their form and function within the merciless theater of natural selection. Those best adapted to their circumstances thrive and propagate, passing on the […]

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Pension Risk Transfer Regulations

Pension Risk Transfer Regulations: A Comparative Analysis

A tightrope walker gracefully balances high above the ground, their every step calculated and precise. One minor misstep, one gust of wind, could send them plummeting from the slim cable providing their precarious passage. This razor-edge act epitomizes the delicate dance between securing hard-earned retirement futures and maintaining overall financial stability. Just as the tightrope […]

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Pension Risk Transfer (PRT) Demystified: Types of Risk and Strategies

Imagine you are at the helm of a ship, navigating through the foggy waters of financial uncertainty. Much like ancient vessels, pension schemes carry the weight of future promises – a secure retirement for those who spent lifetimes in service. Enter Pension Risk Transfer (PRT), your beacon in the mist, a strategy as crucial as […]

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Pension Risk Transfer Explained: Key Concepts and Trends

A pension fund is like a beacon for retirees’ dreams. It represents more than just savings. It is a covenant between the company and its workforce, promising a secure future. But what if, beneath this symbol of stability, a storm is brewing, one that threatens to unsettle the very foundations of their trust and well-being? […]

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Exploring OpenAI’s SORA and Text-to-Video Models: A Complete Guide

In every epoch, some moments redefine the course of human history. The discovery of fire illuminated the dark. The invention of the wheel set humanity in motion. The creation of the printing press unfurled the banners of knowledge across the globe. Unironically, we may be standing at the threshold of another such transformative moment with […]

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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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Videos

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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