ºÝºÝߣshows by User: ScribbleDataMarketin / http://www.slideshare.net/images/logo.gif ºÝºÝߣshows by User: ScribbleDataMarketin / Wed, 15 Mar 2023 05:50:37 GMT ºÝºÝߣShare feed for ºÝºÝߣshows by User: ScribbleDataMarketin 5 Things You Should Know About Data Products /slideshow/5-things-you-should-know-about-data-products/256514537 dataproductpostslidev2-230315055038-6ce19d28
Humans are generating and collecting close to 3.5 quintillion bytes of data every day! This has given rise to data products–from BI dashboards to derived datasets, ML models, and more- bringing data closer to business users. Our Co-founder COO shares his thoughts around data products, and how each data product adds intelligence and efficiency to advanced analytics problems, speeding up analytical throughput by 10x. ]]>
Humans are generating and collecting close to 3.5 quintillion bytes of data every day! This has given rise to data products–from BI dashboards to derived datasets, ML models, and more- bringing data closer to business users. Our Co-founder COO shares his thoughts around data products, and how each data product adds intelligence and efficiency to advanced analytics problems, speeding up analytical throughput by 10x. ]]> Wed, 15 Mar 2023 05:50:37 GMT /slideshow/5-things-you-should-know-about-data-products/256514537 ScribbleDataMarketin@slideshare.net(ScribbleDataMarketin) 5 Things You Should Know About Data Products ScribbleDataMarketin Humans are generating and collecting close to 3.5 quintillion bytes of data every day! This has given rise to data products–from BI dashboards to derived datasets, ML models, and more- bringing data closer to business users. Our Co-founder COO shares his thoughts around data products, and how each data product adds intelligence and efficiency to advanced analytics problems, speeding up analytical throughput by 10x. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dataproductpostslidev2-230315055038-6ce19d28-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Humans are generating and collecting close to 3.5 quintillion bytes of data every day! This has given rise to data products–from BI dashboards to derived datasets, ML models, and more- bringing data closer to business users. Our Co-founder COO shares his thoughts around data products, and how each data product adds intelligence and efficiency to advanced analytics problems, speeding up analytical throughput by 10x.
5 Things You Should Know About Data Products from Scribble Data
]]> 16 0 https://cdn.slidesharecdn.com/ss_thumbnails/dataproductpostslidev2-230315055038-6ce19d28-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Fast Sub-ML Use Case Development Using Feature Stores - Feature Store Summit 2022 /ScribbleDataMarketin/fast-subml-use-case-development-using-feature-stores-feature-store-summit-2022 fssummit22-scribbledata-221025052300-7664235b
Widespread adoption of machine learning (ML) in the industry is still a challenge today due to resource constraints and justifying cost vs. outcomes. ML approaches require high skill and rely on large volumes of data. In this talk, we argue for Sub-ML - a class of applications simpler than traditional ML approaches and often designed to be used in decision support systems. Characterized by their speed in realizing business value and support for diverse use cases, Sub-ML applications still require guarantees of correctness, transparency, and auditability in the data transformation process. In this presentation from the Feature Store Summit 2022, Achint Thomas, Scribble Data's Data Architect shares our experience in the fintech, ed-tech and e-commerce domains to lay out design choices for feature stores to enable Sub-ML, including constraining the problem space, bundling capabilities for fast development, and incorporating a data consumption layer.]]>
Widespread adoption of machine learning (ML) in the industry is still a challenge today due to resource constraints and justifying cost vs. outcomes. ML approaches require high skill and rely on large volumes of data. In this talk, we argue for Sub-ML - a class of applications simpler than traditional ML approaches and often designed to be used in decision support systems. Characterized by their speed in realizing business value and support for diverse use cases, Sub-ML applications still require guarantees of correctness, transparency, and auditability in the data transformation process. In this presentation from the Feature Store Summit 2022, Achint Thomas, Scribble Data's Data Architect shares our experience in the fintech, ed-tech and e-commerce domains to lay out design choices for feature stores to enable Sub-ML, including constraining the problem space, bundling capabilities for fast development, and incorporating a data consumption layer.]]> Tue, 25 Oct 2022 05:22:59 GMT /ScribbleDataMarketin/fast-subml-use-case-development-using-feature-stores-feature-store-summit-2022 ScribbleDataMarketin@slideshare.net(ScribbleDataMarketin) Fast Sub-ML Use Case Development Using Feature Stores - Feature Store Summit 2022 ScribbleDataMarketin Widespread adoption of machine learning (ML) in the industry is still a challenge today due to resource constraints and justifying cost vs. outcomes. ML approaches require high skill and rely on large volumes of data. In this talk, we argue for Sub-ML - a class of applications simpler than traditional ML approaches and often designed to be used in decision support systems. Characterized by their speed in realizing business value and support for diverse use cases, Sub-ML applications still require guarantees of correctness, transparency, and auditability in the data transformation process. In this presentation from the Feature Store Summit 2022, Achint Thomas, Scribble Data's Data Architect shares our experience in the fintech, ed-tech and e-commerce domains to lay out design choices for feature stores to enable Sub-ML, including constraining the problem space, bundling capabilities for fast development, and incorporating a data consumption layer. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/fssummit22-scribbledata-221025052300-7664235b-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Widespread adoption of machine learning (ML) in the industry is still a challenge today due to resource constraints and justifying cost vs. outcomes. ML approaches require high skill and rely on large volumes of data. In this talk, we argue for Sub-ML - a class of applications simpler than traditional ML approaches and often designed to be used in decision support systems. Characterized by their speed in realizing business value and support for diverse use cases, Sub-ML applications still require guarantees of correctness, transparency, and auditability in the data transformation process. In this presentation from the Feature Store Summit 2022, Achint Thomas, Scribble Data&#39;s Data Architect shares our experience in the fintech, ed-tech and e-commerce domains to lay out design choices for feature stores to enable Sub-ML, including constraining the problem space, bundling capabilities for fast development, and incorporating a data consumption layer.
Fast Sub-ML Use Case Development Using Feature Stores - Feature Store Summit 2022 from Scribble Data
]]> 161 0 https://cdn.slidesharecdn.com/ss_thumbnails/fssummit22-scribbledata-221025052300-7664235b-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Feature Store for Sub-ML /slideshow/feature-store-for-subml/251803782 featurestoreforsub-mlv2-220518062325-3b3f787b
Feature stores have been traditionally designed for complex, Big-ML applications that normally assume that there is clear and high-ROI, advanced methods, and skilled staff, all resulting in long lead times. In this presentation, we cover Sub-ML – mid-complexity ML applications. In these, the uncertainty in terms of value, methods used, available staffing is higher, and speed is critical. We see Sub-ML rapidly growing across organizations and functions, which has led to a demand for a different feature store design that caters to the differences in the nature of the problems. In this presentation, we expand on our observations about the problem space, design constraints, and the thinking behind Enrich, our feature store for Sub-ML.]]>
Feature stores have been traditionally designed for complex, Big-ML applications that normally assume that there is clear and high-ROI, advanced methods, and skilled staff, all resulting in long lead times. In this presentation, we cover Sub-ML – mid-complexity ML applications. In these, the uncertainty in terms of value, methods used, available staffing is higher, and speed is critical. We see Sub-ML rapidly growing across organizations and functions, which has led to a demand for a different feature store design that caters to the differences in the nature of the problems. In this presentation, we expand on our observations about the problem space, design constraints, and the thinking behind Enrich, our feature store for Sub-ML.]]> Wed, 18 May 2022 06:23:25 GMT /slideshow/feature-store-for-subml/251803782 ScribbleDataMarketin@slideshare.net(ScribbleDataMarketin) Feature Store for Sub-ML ScribbleDataMarketin Feature stores have been traditionally designed for complex, Big-ML applications that normally assume that there is clear and high-ROI, advanced methods, and skilled staff, all resulting in long lead times. In this presentation, we cover Sub-ML – mid-complexity ML applications. In these, the uncertainty in terms of value, methods used, available staffing is higher, and speed is critical. We see Sub-ML rapidly growing across organizations and functions, which has led to a demand for a different feature store design that caters to the differences in the nature of the problems. In this presentation, we expand on our observations about the problem space, design constraints, and the thinking behind Enrich, our feature store for Sub-ML. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/featurestoreforsub-mlv2-220518062325-3b3f787b-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Feature stores have been traditionally designed for complex, Big-ML applications that normally assume that there is clear and high-ROI, advanced methods, and skilled staff, all resulting in long lead times. In this presentation, we cover Sub-ML – mid-complexity ML applications. In these, the uncertainty in terms of value, methods used, available staffing is higher, and speed is critical. We see Sub-ML rapidly growing across organizations and functions, which has led to a demand for a different feature store design that caters to the differences in the nature of the problems. In this presentation, we expand on our observations about the problem space, design constraints, and the thinking behind Enrich, our feature store for Sub-ML.
Feature Store for Sub-ML from Scribble Data
]]> 290 0 https://cdn.slidesharecdn.com/ss_thumbnails/featurestoreforsub-mlv2-220518062325-3b3f787b-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-ScribbleDataMarketin-48x48.jpg?cb=1678857150 ML Engineering | Feature Store | Trusted Data | MLOps for Data www.scribbledata.io/ https://cdn.slidesharecdn.com/ss_thumbnails/dataproductpostslidev2-230315055038-6ce19d28-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/5-things-you-should-know-about-data-products/256514537 5 Things You Should Kn... https://cdn.slidesharecdn.com/ss_thumbnails/fssummit22-scribbledata-221025052300-7664235b-thumbnail.jpg?width=320&height=320&fit=bounds ScribbleDataMarketin/fast-subml-use-case-development-using-feature-stores-feature-store-summit-2022 Fast Sub-ML Use Case D... https://cdn.slidesharecdn.com/ss_thumbnails/featurestoreforsub-mlv2-220518062325-3b3f787b-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/feature-store-for-subml/251803782 Feature Store for Sub-ML