TL;DR;

Read about MLOps to find out what they are all about. Today, MLOps can help businesses grow and thrive.
The goal of MLOps is to streamline the development, deployment, and operation of machine learning models, by supporting their building, testing, releasing, monitoring, performance tracking, reusing, maintenance and governance, joining the efforts of Data Science and IT teams under a shared focus.

Machine learning is not a technology that has been around for much time, but it’s already giving many benefits and accelerating the digitisation and automation of manufacturers. However, according to research (1), 60% of machine learning models never make it to the production phase, and thus are not implemented, giving considerable losses in terms of costs and resources. This is where MLOps (Machine Learning Operations) comes in, a methodology that comes from DevOps and takes a pipeline from developer to product with a streamline that takes Data Science and IT teams together to develop, deploy, maintain, and scale-out machine learning models.

Even if a business has started recently to explore the possibilities of machine learning models or if they are already using it frequently in their digital journey, leveraging the MLOps methodology is the way to standardise and optimise this process.

What is MLOps and what is its goal?

MLOps, or Machine Learning Operations, is a methodology for communicating and collaborating between IT and Data Science teams, ensuring that the machine learning pipeline is efficient and smooth, removing barriers to make it quickly to production. It’s considered the Agile framework applied to Data Science, and helps to have clear communication between all stakeholders.

MLOps leverages DevOps, but it’s at the same time more than just your typical DevOps. DevOps has been used in software development also as a way to standardise it, as developers code from certain requirements, then building and testing the product before delivering it. However, machine learning models take this a step further, since they need to be trained with real data - this data needs to be kept track of, and as such MLOps has a focus on the versions of data, code, and the machine learning model.

Looking at software development as a comparison, the DevOps pipeline has already become the standard in these processes, based around similar technologies. For machine learning, a similar approach is rising, and leveraging the knowledge and hurdles already faced by businesses to implement DevOps, the learning curve for MLOps is then reduced, even taking into consideration the difficult technology available.

As such, MLOps has become the standard for businesses using machine learning models to help the teams streamline and manage the machine learning lifecycle, breaking down silos between IT and Data Science teams to achieve the same shared business goals.

The goal of MLOps is to streamline the development, deployment, and operation of machine learning models, by supporting their building, testing, releasing, monitoring, performance tracking, reusing, maintenance and governance, joining the efforts of Data Science and IT teams under a shared focus.

What are the benefits of using MLOps?

Since MLOps standardises the machine learning process, there are many advantages to implementing it in the business.

  • Standardisation: When MLOps guidelines are implemented the product is improved, since testing is more robust, and using automation makes development faster. When all the team is working by the same rules, quality of the product is increased too.
  • Communication: MLOps improves the communication between Data Science and IT teams by reducing friction between them, breaking down these silos, and also creates adaptable pipelines that leverage DevOps methodologies to adjust to new machine learning models.
  • Workflows: MLOps strives for optimisation, where models can change automatically through the streamline, measuring and changing behaviours of the model that is being tested, leveraging iteration.
  • Monitoring: MLOps allows for focused monitoring skills, using, for example, data visualisations to ascertain anomalies, helping ensure that the model has high accuracy. MLOps can also help engineers understand and improve them, and evaluate their risk.
  • Complying with regulations: MLOps helps in regulations, which are being tighter than ever, as is the case of GDPR in the EU. MLOps can make models that comply with these standards, and ensure they are always according to regulations.
  • Reduction in bias: MLOps can help reduce the bias in development, like not representing certain audiences by ensuring that certain features do not offset others, as machine learning models adjust to the changes in data with dynamic systems.

In general, MLOps helps with reliability, productivity, and credibility of machine learning development, and takes it to a next step in software development.

What are the challenges of MLOps?

Unlike DevOps, which does not leverage real data, MLOps does take data to train the machine learning models, as this has some challenges to it. In a machine learning model, the code is written to define how to use parameters to solve a problem, and the values for those parameters are discovered through data, that can change with different versions, which affects the code output. This relationship between data and code adds another layer of complexity to machine learning.

The quality of the data is also very important since it’s directly linked to the training of the model, and thus it’s crucial for performance and reducing bias in the output. Another challenge is that, as data increases as more and more data is being caught, the resources may not be sufficient in terms of computing power to predict the machine learning models, creating a bottleneck.

Nevertheless, taking these challenges into consideration it’s still possible to implement smoothly MLOps in an organisation leveraging standard practices.

Examples of machine learning uses

In the manufacturing process, there are many parameters that can be optimised using machine learning models. It’s possible to create recommendations for process parameters and implement a full autonomous optimiser that self learns to pinpoint the best value for every situation. These models can also predict material properties or visual defects, giving recommendations to operators on which settings of the machine to change, and even do it automatically while meeting quality requirements.

For pharma manufactures, for instance, machine learning models can automate visual inspection of medicine foil strips to check for closure, the label information (the brand, ingredients, and so on) and even the physical properties of the capsules (like uniformity, empty capsules, etc.). The specific times or storage requirements of the drugs can also be optimised by leveraging machine learning algorithms.

For these machine learning solutions, leveraging MLOps can then bring faster, more efficient models to the organisation, giving it the best, standardised solution.

Further reading

Cited reports

  1. Why 60 Percent of Machine Learning Projects Are Never Implemented

Broader reading

Learn more about MLOps

Check out this podcast with our CEO, Steph Locke, on MLOps

Listen to podcast