2021 Machine Learning (ML) Business Use Case
As machine learning (ML) As technology improves and use cases multiply, more and more companies are using ML to optimize their operations with data.
Here are some examples from around the world of how organizations from various industries are working with vendors to implement machine learning solutions:
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5 machine learning case studies
1. AES on Google Cloud AutoML Vision
AES Corporation is an electricity generation and distribution company. They produce and sell electricity used for utilities and industrial works.
They rely on Google Cloud to make renewable energy more efficient. AES uses Google AutoML Vision to examine images of wind turbine blades and analyze their maintenance needs.
“In a typical inspection, we come back with 30,000 images,” says Nicholas Osborn, member of the Global AI / ML Project Management Office at AES.
“We have created a great ML solution using the tools and platform from Google Cloud. With the AutoML Vision tool, we have trained it to detect damage. We are able to eliminate approximately half of the images that do not need to be examined by a human. “
Industry: Electricity production and distribution
Machine learning product: Google Cloud AutoML Vision
- Reduced image review time by approximately 50%
- Contributed to reducing the prices of renewable energies
- More time to invest in identifying wind turbine damage and repairing it
Watch the full AES case study on Google Cloud AutoML Vision here.
2. AIMMO Enterprise on Microsoft Azure Machine Learning Studio
AIMMO Enterprise is a South Korean web platform for self-managed data labeling projects. Their services can be used for autonomous driving, robotics, smart factories and logistics.
They were able to increase work efficiency and productivity by establishing an MLOps pipeline using Azure Machine Learning Studio.
“With Azure ML, AIMMO has achieved significant savings and increased business efficiency,” said SeungHyun Kim, CTO of AIMMO.
“By leveraging the Azure ML pipeline, we were able to create the full cycle of the AIMMO MLOps workflow quickly and flexibly. “
Industry: Professional services
Machine learning product: Microsoft Azure Machine Learning Studio
- Improved efficiency and reduced costs
- Helped build the entire AIMMO MLOps workflow
- Facilitates the deployment of batch interface pipelines
- Works as an all-in-one MLOps solution to process 2D and 3D data
Read the full AIMMO case study on Microsoft Azure Machine Learning Studio here.
See more: Key Trends in Machine Learning (ML)
3. Bayer AG on AWS SageMaker
Bayer AG is a multinational pharmaceutical and life sciences company based in Germany. One of their specializations is the production of insecticides, fungicides and herbicides for agricultural purposes.
To help farmers monitor their crops, they created their Digital Yellow Trap: a Internet of Things (IoT) device that alerts farmers to pests using image recognition.
The IoT device is powered by AWS SageMaker, a fully managed service that enables developers to build, train, and deploy machine learning models at scale.
“We have been using Amazon SageMaker for quite some time and it has become one of our core services for AI development,” says Dr Alexander Roth, engineering manager at 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
- 94% reduction in Bayer laboratory architecture costs
- Can be scaled to accommodate fluctuating demand
- Capable of handling tens of thousands of requests per second
- Community early warning system for pests
Read the full Bayer AG case study on AWS SageMaker here.
4. American Cancer Society on Google Cloud ML Engine
The American Cancer Society is a nonprofit organization dedicated to the elimination of cancer. They operate in over 250 regional offices across the United States
They use Google Cloud ML Engine to identify new patterns in digital pathology images. The goal is to improve the accuracy of breast cancer detection and reduce the overall time to diagnosis.
“By leveraging Cloud ML Engine to analyze cancer images, we better understand the complexity of breast tumor tissue and how known risk factors lead to certain patterns,” explains Mia M. Gaudet, Scientific Director of Epidemiological Research to American Cancer. Society.
“Applying digital image analysis to human pathology may reveal new insights into the biology of breast cancer, and Google Cloud is making it easier.”
Industry: Medical and non-profit research
Machine learning product: Google Cloud ML Engine
- Improves the speed and accuracy of image analysis by removing human limitations
- Helps improve the quality of life and life expectancy of patients
- Protect tissue samples by saving image data in the cloud
Read the full American Cancer Society case study on Google Cloud ML Engine here.
5. Western Australian Road Safety Commission on SAS Viya
The Western Australian Road Safety Commission is a business unit of the Police of Western Australia. He is responsible for following road accidents and making the roads safer with adequate precautions.
To carry out its “Towards Zero 2008-2020” safety strategy consisting of reducing the number of road fatalities by 40%, the road safety commission relies on machine learning, artificial intelligence (AI), and advanced analytics.
“The new model assesses intersections based on risk, not accidents,” says David Slack-Smith, data and intelligence manager at the Road Safety Commission of Western Australia.
“Taking out the variability and analyzing by risk is a fundamental change in the way we look at this problem and make recommendations to reduce the risk. “
Industry: Government and transport
Machine learning product: SAS Viya
- 80% reduction in engineering and data visualization time
- An estimated 25% reduction in vehicle accidents
- Simple and efficient data sharing
- Data flexibility with different coding languages
Read the full Road Safety Commission case study on SAS Viya here.
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