Suppliers Israel Iguazio
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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.
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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.
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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.
Analytics & Modeling
Machine Learning
Real Time Analytics
Platform as a Service (PaaS)
Data Management Platforms
Use Cases
Edge Computing & Edge Intelligence
Finance & Insurance
Iguazio’s Technology Stack maps Iguazio’s participation in the platform as a service (paas), and analytics and modeling IoT technology stack.
  • Application Layer
  • Functional Applications
  • Cloud Layer
  • Platform as a Service
    Infrastructure as a Service
  • Edge Layer
  • Automation & Control
    Processors & Edge Intelligence
  • Devices Layer
  • Robots
  • Supporting Technologies
  • Analytics & Modeling
    Application Infrastructure & Middleware
    Cybersecurity & Privacy
    Networks & Connectivity
Technological Capability
Number of Case Studies5
Quadient Leverages Iguazio for Real-Time Machine Learning
Quadient, a leading provider of omnichannel customer experience solutions, needed a way to unify and combine every single data type they work with to create machine learning applications that run in real time. This would enable its developers to build a SAAS cloud platform for enterprises to improve customer experience and achieve digital transformation through business automation. Its platform would then be able to predict events by ingesting data from several sources including real-time sensor data, historical data (like ERP and CRM), news, social media, flight tracking, and other sources — and then leverage AI and ML to interact with all that layered data to derive business interaction predictions. This enabled Quadient to provide new capabilities and additional value to its clients (particularly those in the insurance space). Prior to finding and leveraging Iguazio, Quadient tested several cloud platforms to unify, store, and provide a single interface for the various data types it wanted to work with (such as relational data, key values, time series, etc.)
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.
Hygiene technologies leader Ecolab brings data science to production with Microsoft Azure and Iguazio
Ecolab, a global leader in water, hygiene, and infection prevention solutions, wanted to develop predictive risk models for water systems, industrial machinery, and other applications. The company's machine learning journey began in 2016 with a project to develop bacterial growth risk models using existing sensor data. However, the process of building, deploying, and maintaining machine learning models in production was complex and challenging. The company needed a data science collaboration platform that would bring together its large, geographically dispersed team, while efficiently using cloud computing resources. The deployment of machine learning models at Ecolab followed a 'rewrite-and-deploy' pattern, where model development occurred independent of the application developers. This approach led to deployment timelines exceeding 12 months on average.
Number of Similar Suppliers5
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DotData delivers an end-to-end Data Science automation for the enterprise. Its Data Science automation platform speeds time to value by accelerating, democratizing, and operationalizing the entire Data Science process through automation. Founded in 2018, dotData is a spin off of NEC Corporation, and is led by Dr. Ryohei Fujimaki, a data scientist, and the youngest research fellow ever appointed in the 119-year history of NEC.
Alteryx, Inc. was formed in 2011 and is a leader in self-service Data Science and analytics. Alteryx provides analysts with the unique ability to easily prep, blend and analyze all of their data using a repeatable workflow, then deploy and share analytics at scale for deeper insights in hours, not weeks.Analysts love the Alteryx Analytics platform because they can connect to and cleanse data from data warehouses, cloud applications, spreadsheets and other sources, easily join this data together, then perform analytics – predictive, statistical and spatial – using the same intuitive user interface, without writing any code.
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