What is MLOps?
MLOps is an important integration for all organizations, who use machine learning models in production. By implementing MLOps in an organization you will be able to ensure that their ML models are reliable, efficient and scalable.
Benefits by MLOps
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Improved quality in machine learning models
MLOps will ensure that the efficiency in the models is trained and evaluated upon, which leads to more reliable and precise results. -
Improved efficiency
MLOps automate many manual assignments that are connected to the implementation and maintenance of machine learning models. By automating these processes you will be able to save time and resources. -
Reduced risk
MLOps helps identify and handle potential problems with the machine learning models, before the cause breakdowns.
Are you interested in learning more about our MLOps services?
How can Neurospace help you?
We are a team of experienced MLOps specialists and we strive to always deliver good results. We can help you get started by:
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Assess your needs
We always collaborate with our partners to ensure that you become a specialist in your MLOps integration. -
Design a MLOps-strategy
In collaboration with your organization, we tailor a strategy that is aligned with your specific needs and requirements. -
Implementation of MLOps
We work agile and are able to implement MLOps integration based on a technology after your choosing. -
We will onboard you to monitor and maintain
We believe in the importance of onboarding employees from your organization to be able to monitor and maintain your MLOps solution to ensure it runs smoothly.
Machine Learning in practice
Get inspired by how others have implemented data and machine learning models to improve their business. Read our Partner cases here:
Forecasting models
OWLS - Optimal Vaccum Vapour Level Setpoint
- Significant scaling potential
- The model reduces energy consumption in the vacuum pan by using more efficient energy steam, when possible
- The model was implemented in January 2024 and has already had a positive impact on production.
Nordic sugar is now able to identify untapped potential in the fact that the cycle time around the vacuum pan equipment was often not fully utilized. This knowledge creates an optimization potential of 4-43%.
Estimating Remaining Useful Life on a Steam Lock
- The costumer should be better able to document different types of breakdowns
- They may not have as many breakdowns as the thought and the breakdowns are likely to be sudden
- There is potential for predictive maintenance.
Historical breakdown data was used to estimate when a new breakdown will occur. The solution makes it possible to schedule maintenance outside of critical production periods.
Predicting Optimal Temperature in the Transmission System
- Predict the optimal supply temperature 12 hours in advance
- Increase efficiency in spring and autumn when the weather is particularly changeable
- Potentially reduce the supply temperature by an average of 5.4 degrees
CTR is, among other things, to be able to deliver greener and more climate-neutral district heating.
Predicting Unwanted Situations in the Transmission System
- Identification of key markers for predicting critical situations
- Development of a system that can provide predictions 12 hours in advance
- Increased transparency around unwanted situations
Kredsløb can monitor and warn about unwanted situations in remote ports of their transmissions system
Computer Vision
Optimize Quality Control with Computer Vision in Danish Crown
- The possibility of automating quality control with computer vision
- An accuracy of 58.5% on images of pork roasts, which is a success given a small dataset, in which poor quality roasts are underpresented.
Danish Crown has gained the knowledge that quality is subjective and difficult to define, but there is potential to automate quality control with computer vision.
