Industry Archives

Creating an effective price markdown strategy using Machine Learning

In this demo video, we explain how the price markdown for a retail store can be done using Machine Learning and how to visualize them using Looker Dashboard. We also give an overview of the preprocessing of data, features opted for building models, and the model built. The demo also gives extensive insights on the methods …

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Smart Government Series – The Power of Machine Learning for Public Works

In this first episode of our Smart Government podcast series, we talk to Andrew Larimer, Practice Manager at SpringML. Andrew explains how some of the Vision AI use cases like pothole detection, combating urban blight, etc. help governments improve the quality of life for their citizens. He also addresses the challenges that may arise with the …

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Using Digital Twin Capability to Improve Retail Supply Chain Transparency

Digital twins - Bridging the physical and digital. This twin would enable one to collaborate virtually, intake sensor data and simulate conditions quickly, understand what-if scenarios clearly, predict results more accurately, and output instructions to manipulate the physical world. Digital twins can simulate any aspect of a physical object or process. Digital twin capabilities are not …

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SpringML wins 2020 Google Cloud Public Sector Partner of the Year – State and Local Governments Award

Pleasanton, California. July 20, 2021 — SpringML, Inc. (“SpringML”) today announced that it has received the 2020 Google Cloud Public Sector Partner of the Year - State and Local Governments Award. SpringML was recognized for the company’s achievements in the Google Cloud ecosystem, helping public sector customers fight against the pandemic using Google Cloud technologies. “We …

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Using Google Maps Tech For Liability Management in Public Works Departments

Introduction: SpringML was recently awarded the status of being the Top Tier Partner for solutions requiring Google Maps Expertise. We were glad to leverage our knowledge and understanding in the domain to solve customer concerns around liability management, specifically for Public Works departments. For example, SpringML used machine learning models and Google Maps to help the …

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Predictive Pricing Model for Retail using Einstein Discovery and Tableau CRM

Pricing is an essential aspect for any business that shows how little optimization impacts your profits. Machine learning models and visualization dashboards have enabled us to determine the balance between the perceived value and actual value. In this demo video, we walk you through a retail use case for pricing specific products using Einstein Discovery and …

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Getting started with GraphQL

GraphQL is a query language and server-side runtime for APIs that prioritizes fetching the exact requested data for the clients. It also makes APIs faster, flexible, and developer-friendly and enables the developers to construct requests to pull data from multiple data sources in a single API call. In this demo video, we take you through a …

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Vehicle Performance and Safety Evaluation using Vehicle Tracker Application

Vehicle Tracker is a web application created for users to view the vehicle tracking information, downtime, safety and operator history of each site, and maintenance history of various vehicles used in the fields of mining and construction. This application also provides the next best actions, maintenance recommendations to the users. Watch the video for more details. …

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Online Medical Consultation using Twilio

In this SpringML demo video, we'll be walking through a video chat room application built using Twilio. The objective of this application is to ease the online medical consultation between doctors and their patients. Watch this demo video for more details Related Blogs Google Cloud Platform Solution for SAP(IBP) Integration AI-Powered Order Processing and Order Refund …

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Medical Imaging Part 3: How to Use SpringML’s Ready to Deploy Solution

This is a cheat sheet to guide you through our solution, the features and its respective functionalities. This UI provides granular control over the Dataset names, DICOM store names, GCS buckets, tags to be identified both at the metadata level and the image level. Steps to be followed: Enter valid Dataset names which would be used …

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