Resources

Blog posts

What is the Metadata Economy?

We live in a hyper-digital world, and due to the nearly  infinite number of data sources that surround us, the volume of data generated collectively by individuals, applications and corporations is larger than ever. With such a monumental amount of data to sift through, two core principles have  become increasingly important: Metadata – Make it […]

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Data Science Teams are Doing it Wrong: Putting Technology Ahead of People

Despite $200+ billion spent on ML tools, data science teams still struggle to productionize their data and ML models. We decided to do a deep dive and find out why.  Back in 1991, former US Air Force pilot and noted strategist John Boyd called for U.S. Military reforms after Operation Desert Storm. He noted that […]

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MLOps – The CEO’s Guide to Productionization of Data [Part 2]

With data being touted as the oil for digital transformation in the 21st century, organizations are increasingly looking to extract insights from their data by building and deploying their custom-built ML models. In our previous article (MLOps – The CEO’s Guide to Productionization of Data, Part 1), we learned why and how embedding ML models […]

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MLOps – The CEO’s Guide to Productionization of Data [Part 1]

MLOps (or Machine Learning Operations) is a core function of Machine Learning engineering, that focuses on streamlining the process of taking ML models to production, and maintaining and monitoring them.  But before we get into more details about MLOps, it’s important to understand what operationalization of machine learning is, why it’s important, and how it […]

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Scribble Data at the Feature Store Summit 2022

Over the past 3 years, we’ve heard a lot about Feature Stores. While they might not sound like much, over time, they’ve become table stakes for enterprises building their offerings on ML.  The rapid adoption of feature stores, where they’re starting to become mainstream instead of being a niche restricted to big-tech, can largely be […]

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What is Anomaly Detection?

Anomaly detection refers to the process of analysing data sets to detect unusual patterns and outliers that do not conform to expectations.  It takes on even more importance in a world where enterprises depend heavily on an intricate web of distributed systems. With thousands of potentially important data items to monitor every second, it is […]

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Feature Stores: The CEO’s Guide

As industries across the globe attempt to adapt to the big data architecture, expensive and ineffective feature engineering practices mean that businesses are very likely to “hit a wall” when it comes to organizing their machine learning operations (MLOps). A lot of time is consumed in data ingestion, and lackluster machine outputs indicate that stakeholders […]

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How Postmodern Data Stack helps Fintech companies make faster decisions

The Fintech market is valued at $110.57 billion in 2020 and will reach $698.48 billion by 2030. It is one of the fastest-growing industries with a CAGR of 20.3%. Fintech companies faced a surge in demand as customer practices and banking habits changed during the COVID-19 era. The industry overall saw an increase in user […]

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Map Contextual Data as an input to Business Outcomes as an output

Machine learning and data science today are in a unique position where access to capital is often not the biggest barrier to success. Companies globally are continuing to invest into artificial intelligence to the tune of $140 billion, either to develop AI-native products or solutions or as a way to solve business problems and improve […]

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Case Studies

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

Learn how Mars, a US-based multinational CPG manufacturer of confectionery, pet food, and other food products and a provider of animal care services collaborated with Scribble Data to assign a “probability of attrition” based on which employees were quitting and their motivating factors through data, and ML modeling.

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Videos

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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DevOps for Machine Learning Projects

In this session, Dmitry Pretrov and Ivan Shcheklein – co-founders of DVC – discuss: How software engineering principles apply to Machine Learning development and deployment. How ML systems are different from traditional applications. Importance of data versioning.

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