As machine learning (ML) technology improves and uses cases grow, more businesses are employing ML to optimize their operations through data.

Here are some examples across the globe of how organizations in various industries are working with vendors to implement machine learning solutions:

See more: Machine Learning Market

5 Case Studies On Machine Learning

1. AES On Google Cloud AutoML Vision

The AES Corporation is a power generation and distribution company. They generate and sell power used for utilities and industrial work.

They relies on Google Cloud on their road to making renewable energy more efficient. AES uses Google AutoML Vision to review images of wind turbine blades and analyze their maintenance needs.

“On a typical inspection, we’re coming back with 30,000 images,” says Nicholas Osborn, part of the Global AI/ML Project Management Office at AES.

“We’ve built a great ML solution using Google Cloud’s tools and platform. With the AutoML Vision tool, we’ve trained it to detect damage. We’re able to eliminate approximately half of the images from needing human review.”

Industry: Electric power generation and distribution

Machine learning product: Google Cloud AutoML Vision

Outcomes:

• Reduced image review time by approximately 50%
• Helped reduce prices of renewable energy
• More time to invest in identifying wind turbine damage and mending it

Watch the full AES on Google Cloud AutoML Vision case study here.

2. AIMMO Enterprise On Microsoft Azure Machine Learning Studio

AIMMO Enterprise is a South Korean web-based platform for self-managing data labeling projects. Their services can be used for autonomous driving, robotics, smart factories, and logistics.

They were able to boost work efficiency and productivity by establishing an MLOps pipeline using the Azure Machine Learning Studio.

“With Azure ML, AIMMO has experienced significant cost savings and increased business efficiency,” says SeungHyun Kim, chief technical officer at AIMMO.

“By leveraging the Azure ML pipeline, we were able to build the entire cycle of AIMMO MLOps workflow quickly and flexibly.”

Industry: Professional services

Machine learning product: Microsoft Azure Machine Learning Studio

Outcomes:

• Improved efficiency and reduced costs
• Helped build AIMMO’s entire MLOps workflow
• Makes it easier to deploy batch interface pipelines
• Works as an all-in-one MLOps solution to process data in 2D and 3D

Read the full AIMMO on Microsoft Azure Machine Learning Studio case study here.

See more: Key Machine Learning (ML) Trends

3. Bayer AG On AWS SageMaker

Bayer AG is a multinational pharmaceutical and life sciences company based in Germany. One of their specializations is in producing insecticides, fungicides, and herbicides for agricultural purposes.

To help farmers monitor their crops, they created their Digital Yellow Trap: an Internet of Things (IoT) device that alerts farmers of pests using image recognition.

The IoT device is powered using AWS’ SageMaker, a fully managed service that allows developers to build, train, and deploy machine learning models at scale.

“We’ve been using Amazon SageMaker for quite some time, and it’s become one of our crucial services for AI development,” says Dr. Alexander Roth, head of engineering at the Crop Protection Innovation Lab, Bayer AG.

“AWS is constantly improving its services, so we always get new updates.”

Industry: Agriculture and pharmaceuticals

Machine learning product: AWS SageMaker

Outcomes:

• Reduced Bayer lab’s architecture costs by 94%
• Can be scaled to accommodate for fluctuating demand
• Able to handle tens of thousands of requests per second
• Community-based, early warning system for pests

Read the full Bayer AG on AWS SageMaker case study here.

4. American Cancer Society On Google Cloud ML Engine

The American Cancer Society is a nonprofit dedicated to eliminating cancer. They operate in more than 250 regional offices all over the U.S.

They’re using Google Cloud ML Engine to identify novel patterns in digital pathology images. The aim is to improve breast cancer detection accuracy and reduce the overall diagnosis timeline.

“By leveraging Cloud ML Engine to analyze cancer images, we’re gaining more understanding of the complexity of breast tumor tissues and how known risk factors lead to certain patterns,” says Mia M. Gaudet, scientific director of epidemiology research at the American Cancer Society.

“Applying digital image analysis to human pathology may reveal new insights into the biology of breast cancer, and Google Cloud makes it easier.”

Industry: Nonprofit and medical research

Machine learning Product: Google Cloud ML Engine

Outcomes:

• Enhances speed and accuracy of image analysis by removing human limitations
• Aids in improving patients’ quality of life and life expectancy
• Protects tissue samples by backing up image data to the cloud

Read the full American Cancer Society on Google Cloud ML Engine case study here.

5. Road Safety Commission Of Western Australia On SAS Viya

The Road Safety Commission of Western Australia is a business unit of the Western Australia Police Force. It is responsible for tracking road accidents and making the roads safer with adequate precautions.

To achieve its safety strategy “Towards Zero 2008-2020” of reducing road fatalities by 40%, the road safety commission is relying on machine learning, artificial intelligence (AI), and advanced analytics.

“The new model assesses intersections by risk, not by crashes,” says David Slack-Smith, manager of data and intelligence at the Road Safety Commission of Western Australia.

“Taking out the variability and analyzing by risk is a fundamental shift in how we look at this problem and make recommendations to reduce risk.”

Industry: Government and transportation

Machine learning product: SAS Viya

Outcomes:

• Data engineering and visualization time reduced by 80%
• An estimated 25% reduction in vehicle crashes
• Straightforward and efficient data sharing
• Flexibility of data with various coding languages