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Improved Sales for a Machine Learning Startup

Improved Sales for a Machine Learning Startup

Problem

  • Our client is a leading machine learning and analytics startup that provides machine learning based software for quantifying risk by analyzing e-commerce transactions.
  • In order to enable evaluation of their software, their customers needed to use their data. The software had to be installed at their customer data centers (public/private cloud) & that required some dedicated time from prospect/customers.
  • This meant customers couldn’t see proof of concept analysis on their data.
  • This was one of the main reasons their PoCs were extending the sales cycles.

Solution

  • CloudHedge team analyzed the product, identified components & converted them into Docker container-based products.
  • Once the docker containers were created, we could use CloudHedge Cruize service to deploy those containers within minutes directly into customer’s public cloud (Azure, GCP or AWS).


Benefits

  • Client’s development team did not have to handhold customers/prospects for setting up PoC.
  • The docker based product setups enabled more PoCs that the previous method, that was dependent on availability of key personnel.
  • The sales cycle bottleneck was reduced & it was possible for quickly demonstrating the value of the product without getting bogged down by setup steps or logistical roadblocks.

Tools Used

  • Kubernetes, Docker
  • CloudHedge Cruize
  • CloudHedge Transform
  • Python
  • Kafka
  • React

Platforms

  • AWS, Google Cloud, Azure Cloud

Author:

Sameer Karmarkar

Sameer is an experienced software professional with a passion for building and delivering business solutions and is the CTO of CloudHedge. His current interests include virtualization, containers, mobile web and cloud computing (specifically adoption). In his free time, Sameer loves to travel and explore new places.

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