Rio Tinto Adopts MLOps to Accelerate Machine Learning Advancements
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Quick Read
- Rio Tinto is utilizing MLOps to simplify the deployment of machine learning models.
- The major mining company is employing Amazon SageMaker Studio and SageMaker Canvas for machine learning tasks.
- Key functional domains reaping advantages from machine learning encompass predictive maintenance, health and safety, as well as habitat management.
- A key emphasis is placed on data accessibility and security, which includes utilizing AWS PrivateLinks.
- Rio Tinto has formed specialized machine learning teams around the world, with locations in India, Australia, Singapore, and Canada.
Standardizing Machine Learning Processes
Marcus Rosen, a principal in machine learning operations (MLOps), revealed at the AWS Summit in Sydney that Rio Tinto is adopting MLOps to standardize the delivery and deployment of new machine learning models. The goal is to make the tasks of data science teams easier by simplifying complex infrastructure and security challenges.
Centralised ML Capability
Seven years ago, Rio Tinto launched a centralized machine learning program aimed at aiding different business divisions and product teams. This program provides support to data scientists and citizen data users based in Brisbane, Perth, Singapore, Montreal, as well as a dedicated team in India.
Utilising Amazon SageMaker
The organization utilizes Amazon SageMaker Studio and SageMaker Canvas to assist both its data scientists and non-technical users. SageMaker Canvas stands out for its ability to allow ‘citizen data scientists’ to create machine learning models without requiring significant coding proficiency.
Automating ML Processes
MLOps in SageMaker streamlines and standardizes workflows throughout the machine learning lifecycle. This method enables data science teams to concentrate on their main responsibilities without getting distracted by infrastructure issues.
Accessibility and Protection of Data
Having access to high-quality data is essential for creating effective machine learning models. Nevertheless, Rio Tinto encounters difficulties with several data lakes and strict network controls. The production environments are air-gapped and necessitate whitelisting through a centralized firewall for any external connections.
Multi-cloud Data Lakehouse Solution
The company is working on creating a multicloud data lakehouse platform that empowers teams to independently manage and publish their datasets using a data mesh approach. In this framework, datasets are considered products handled by specialized teams, which guarantees controlled access and meticulous curation.
Leveraging AWS PrivateLinks
Rio Tinto extensively uses AWS PrivateLinks to ensure secure access to data housed in AWS cloud environments. This approach streamlines security and data access for teams, resulting in greater automation and efficiency.
The Influence of Machine Learning on Operations
Machine learning has shown considerable advantages in different operational domains at Rio Tinto.
Predictive Maintenance
A machine learning model forecasts maintenance requirements on the private rail network utilized for ore transport. This model anticipates potential problems up to seven weeks ahead, enabling proactive planning and maintenance to prevent expensive interruptions.
Health and Safety
In Canada, a machine learning model forecasts the probability of water leaks in industrial smelters, which have the potential to cause explosive hydrogen gas accumulations. This model delivers hourly risk assessments that integrate into the plant’s risk management system, thereby improving safety protocols.
Habitat Management
Rio Tinto uses machine learning to locate and maintain animal habitats near its mining areas. This data is incorporated into the planning process to reduce environmental disruptions.
Summary
Rio Tinto’s implementation of MLOps via Amazon SageMaker has optimized its machine learning processes, enabling data science teams to deploy models more effectively. Emphasizing automation, data accessibility, and security, the company has seamlessly incorporated ML into essential operational sectors, such as predictive maintenance, health and safety, and habitat management. This integration showcases the significant impact of these technologies within the mining industry.
Q&A Session
What does MLOps refer to?
A:
MLOps, an abbreviation for Machine Learning Operations, refers to the practice focused on optimizing and automating the deployment, monitoring, and management of machine learning models within production settings.
Why did Rio Tinto select Amazon SageMaker?
A:
Rio Tinto selected Amazon SageMaker due to its extensive tools such as SageMaker Studio and SageMaker Canvas, which make it easier for both technical and non-technical users to develop and deploy machine learning models.
Q: What advantages does Rio Tinto gain from implementing predictive maintenance?
A:
Using machine learning models for predictive maintenance, Rio Tinto can anticipate potential problems in its rail network as early as seven weeks ahead. This forward-thinking strategy reduces expensive interruptions by enabling prompt maintenance actions.
What obstacles does Rio Tinto encounter regarding data accessibility?
A:
Rio Tinto encounters difficulties with numerous data repositories and strict security protocols, such as isolated production accounts. These issues hinder access to essential datasets required for developing efficient machine learning models.
What does a multicloud data lakehouse platform refer to?
A:
A multicloud data lakehouse platform integrates the features of a data lake and a data warehouse across various cloud infrastructures. This allows teams to independently service and share their datasets effectively, while maintaining regulated access and oversight.
Q: In what ways does machine learning improve health and safety at Rio Tinto?
A:
A machine learning model in Canada forecasts water leaks in industrial smelters, which might lead to hydrogen gas accumulation. By delivering risk ratings every hour, this model improves safety protocols and helps avert possible dangers.
Q: How is machine learning utilized in habitat management?
A:
Machine learning aids Rio Tinto in identifying and managing animal habitats near its mining areas. This data is utilized in planning processes to prevent disruptions to these habitats, showcasing the company’s dedication to ecological responsibility.
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