Resources
Blogs, videos, case studies and announcements from our team
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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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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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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Understanding the Advanced Data Analytics Lifecycle
Businesses around the world generate massive quantities of data daily in the form of server logs, web analytics, transactional information, and customer data. To effectively process this much information and derive actual value from it, businesses need to consider advanced analytics techniques for decision-making. We already discussed its applications across industries in our previous article. […]
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Unlocking the Potential of Advanced Analytics
Data is the most important asset for any modern organization, backing most business-critical decisions today. However, fully capturing the potential of the company’s data sources, so that they start yielding impactful business insights, is not a straightforward task and the traditional BI and analytics stack is just not at the level to handle the complex […]
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2023: A Critical Year for ML’s Rapid Growth
As 2022 draws to a close, it is time to reflect on the year gone by and welcome 2023! I’d like to take this opportunity to talk about some of the highs, the lows, the opportunities and learnings in 2022, how we’ve seen the market evolving, how it’s impacted some of the choices we’ve made […]
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Security in ML Systems using Feature Stores
With the transformational early successes in value creation, AI/ML is set to become ubiquitous. By 2030, AI could potentially contribute up to $15.7tr to the global economy. As more and more organizations are depending on data and Machine Learning (ML) models for their crucial decision-making, the security of data and these ML systems is business […]
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Our Learnings in Getting SOC 2 Type II Certified as a Startup
The SOC 2 certification process is considered to be painstaking, but it doesn’t need to be. We share our experience in this one-stop guide for other startups that are considering becoming SOC 2 Type II certified. Every day, Analytics and Data Science teams across the globe trust Scribble Data to solve persistent business problems with […]
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Scribble Data Earns SOC 2 Type II Compliance Certification
The SOC 2 certification validates the makers of Enrich full-stack feature engineering platform as a reliable data partner that ensures the safety and privacy of customer data. TORONTO, DECEMBER 5, 2022 Scribble Data, maker of Enrich, a full-stack feature engineering platform for analytics, has successfully achieved SOC 2 Type II certification after completing a third-party […]
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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 […]
Read MoreCase 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.
Read MoreVideos
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!
Watch NowCustomer 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 […]
Watch NowWhat’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?
Watch NowAnatomy 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 […]
Watch NowAccelerating 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.
Watch NowGlobal 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 […]
Watch NowOperationalizing 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.
Watch NowExperimentation 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.
Watch NowDevOps 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.
Watch NowMLOps Coffee Sessions #4: A Conversation Around Feature Stores with Venkata Pingali and Jim Dowling
We asked what you wanted to hear next on our Coffee sessions and the vote was in favor of feature stores! Today the usual suspects Demetrios Brinkmann and David Aponte sat down to talk with Jim Dowling CEO of Logical Clocks and Venkata Pingali CEO of scribble data to talk about feature stores, what they […]
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