Iguazio Case Studies Hygiene technologies leader Ecolab brings data science to production with Microsoft Azure and Iguazio

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	Hygiene technologies leader Ecolab brings data science to production with Microsoft Azure and Iguazio Iguazio

Hygiene technologies leader Ecolab brings data science to production with Microsoft Azure and Iguazio

Iguazio

Technology Category	Analytics & Modeling - Machine Learning Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries	Chemicals
Applicable Functions	Discrete Manufacturing Quality Assurance
Use Cases	Predictive Maintenance Process Control & Optimization
Services	Data Science Services System Integration
Challenge	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. Read More
About Customer	Ecolab is a major chemicals manufacturer that specializes in water treatment and safety. The company is headquartered in St. Paul, Minnesota and operates in more than 170 countries. Ecolab employs 44,000 people and serves nearly 3 million customer locations in the food, healthcare, hospitality, and industrial markets. The company is driven by a mission to make the world cleaner, safer, and healthier, helping customers succeed while protecting people and vital resources. Read More
Solution	Ecolab turned to Microsoft Azure and MLOps expert Iguazio to reduce its machine learning development and deployment times, cut costs, and enable its geographically dispersed team to collaborate seamlessly on accelerating the rollout of AI applications. The Iguazio solution, built on an open-source architecture that includes Kubeflow, was readily adopted to extend and simplify the process. Ecolab used Iguazio in combination with Git and Azure DevOps to develop the feature engineering pipelines and machine learning applications. The solution also brings direct visibility into the code that is running during production. The deployment of the Iguazio platform coincided with the eruption of the COVID-19 health crisis, fueling a dramatic increase in demand to incorporate data science models into Ecolab’s broader digital offerings. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Platform as a Service (PaaS) - Data Management Platforms

Applicable Industries

Chemicals

Applicable Functions

Discrete Manufacturing

Quality Assurance

Use Cases

Predictive Maintenance

Process Control & Optimization

Services

Data Science Services

System Integration

Challenge

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.

About Customer

Ecolab is a major chemicals manufacturer that specializes in water treatment and safety. The company is headquartered in St. Paul, Minnesota and operates in more than 170 countries. Ecolab employs 44,000 people and serves nearly 3 million customer locations in the food, healthcare, hospitality, and industrial markets. The company is driven by a mission to make the world cleaner, safer, and healthier, helping customers succeed while protecting people and vital resources.

Solution

Ecolab turned to Microsoft Azure and MLOps expert Iguazio to reduce its machine learning development and deployment times, cut costs, and enable its geographically dispersed team to collaborate seamlessly on accelerating the rollout of AI applications. The Iguazio solution, built on an open-source architecture that includes Kubeflow, was readily adopted to extend and simplify the process. Ecolab used Iguazio in combination with Git and Azure DevOps to develop the feature engineering pipelines and machine learning applications. The solution also brings direct visibility into the code that is running during production. The deployment of the Iguazio platform coincided with the eruption of the COVID-19 health crisis, fueling a dramatic increase in demand to incorporate data science models into Ecolab’s broader digital offerings.

Impact #1	With the adoption of the Iguazio platform, deployment times for highly secure, scalable compute resources decreased from days to minutes.
Impact #2	Ecolab was able to reduce costs by running in a shared compute environment.
Impact #3	Model development time dropped dramatically. Prior to implementing this solution, model deployment times exceeded 12 months. Thanks to Iguazio and Microsoft Azure, by 2020, initial deployment timelines had been reduced to between 30 and 90 days.

Impact #1

With the adoption of the Iguazio platform, deployment times for highly secure, scalable compute resources decreased from days to minutes.

Impact #2

Ecolab was able to reduce costs by running in a shared compute environment.

Impact #3

Model development time dropped dramatically. Prior to implementing this solution, model deployment times exceeded 12 months. Thanks to Iguazio and Microsoft Azure, by 2020, initial deployment timelines had been reduced to between 30 and 90 days.

Benefit #1	Deployment times for compute resources decreased from days to minutes.
Benefit #2	Model development time reduced from over 12 months to between 30 and 90 days.
Benefit #3	By the end of 2020, Ecolab had more than 40 users—more than double what it had anticipated. And three to four times more models running than originally planned.

Benefit #1

Deployment times for compute resources decreased from days to minutes.

Benefit #2

Model development time reduced from over 12 months to between 30 and 90 days.

Benefit #3

By the end of 2020, Ecolab had more than 40 users—more than double what it had anticipated. And three to four times more models running than originally planned.

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Overview

Hygiene technologies leader Ecolab brings data science to production with Microsoft Azure and Iguazio

Operational Impact

Quantitative Benefit