Iguazio
Overview
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HQ Location
Israel
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Year Founded
2014
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Company Type
Private
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Revenue
$10-100m
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Employees
51 - 200
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Website
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Twitter Handle
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Company Description
Data science is too important to today’s businesses to be held back by delays and inefficiencies. Iguazio was created to remove the obstacles preventing data science from seeing the light of day, helping teams seamlessly implement their creations into business applications and make game-changing impact on their industry.
IoT Solutions
THE IGUAZIO DATA SCIENCE PLATFORM POWERS End-to-End Machine Learning Pipelines, automates and accelerates complete machine learning workflows, cutting the time to impact of your data science creations.
IoT Snapshot
Iguazio is a provider of Industrial IoT platform as a service (paas), and analytics and modeling technologies, and also active in the finance and insurance, retail, and telecommunications industries.
Technology Stack
Iguazio’s Technology Stack maps Iguazio’s participation in the platform as a service (paas), and analytics and modeling IoT Technology stack.
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Devices Layer
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Edge Layer
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Cloud Layer
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Application Layer
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Supporting Technologies
Technological Capability:
None
Minor
Moderate
Strong
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Case Studies.
Case Study
HCI’s Journey to MLOps Efficiency: A Case Study
Home Credit International (HCI), a global consumer financial provider, recognized the potential of Machine Learning (ML) models in financial institutions, particularly in risk-related use cases. However, they faced challenges in deploying ML models efficiently. The time to delivery was long and access to data was limited. HCI’s internal research revealed that nearly 80% of the time spent on data science-related tasks was dedicated to collecting datasets and cleaning and organizing the data, leaving only about 20% of the time for core tasks like building training sets, mining data, and refining algorithms. In 2021, the average delivery time of an AI initiative, from prototype to production, was more than seven months. The biggest blocker for more efficient use of AI/ML was access to data, followed by the need for a proper AI/ML environment.
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