To illustrate this with an example, suppose you’re deploying an ML model that uses a vast amount of historic data on commuter traffic to predict the number of trips from one city to another in the near future. Your model works great and does the job quite precisely. However, when the Covid19-lockdown came, the commuting behavior of citizens was disrupted big time. Unfortunately, your Machine Learning model kept making predictions as if nothing had happened. Making models self-learn in a real-life, complex world is definitely not as obvious as it is often presented.

Another example is the rise in prices of goods due to higher material costs that were caused by the pandemic and the resulting global supply chain disruptions. How could a static ML model account for this? Short answer: it can't. Data in a company is a moving target, and if you want to hit the bullseye every time, your models should adjust too. 

At Faktion, we want to build Machine Learning applications for our clients that evolve over time as they interact with the outside world. To do so, we take the lead in the transformation from training static models to building continual learning models. 

Introducing MLOps

When building an ML model, there are a lot of steps to go through, from defining the business objectives and designing an application, to model development, to eventually deploying and monitoring the model. In each of those steps, different teams of experts are involved, creating gaps in the flow, and in every step, some things are still done manually.

To increase the efficiency of the whole process, we’d like to automate the complete Machine Learning workflow without having to interrupt it manually. That's where Machine Learning Operations, or MLOps for short, comes into play. It’s a discipline that aims at facilitating the development, deployment, and monitoring of Machine Learning systems. 

We’re aiming for a situation where everything from data validation to model deployment is fully automated. MLOps is based on four components: Continuous Training (CT), Continuous Integration (CI), Continuous Delivery (CD), and Continuous Monitoring (CM). Continuous Training means that the model is automatically retrained when a certain quantity of new data is available. In this step, we also keep track of hyperparameters, dataset version, and performance metrics. The next step, Continuous Integration, will auto-validate the retrained model by means of running automated performance and end-to-end tests. The third stage, Continuous Delivery, suggests that after a model has been tested, it can be easily deployed, either fully automatic or with a human in the loop. Finally, Continuous Monitoring covers tracking and visualizing the model output and performance metrics.

Benefits of MLOps

MLOps brings a lot of advantages to the table, some of them more groundbreaking than others. For example, because of the automation of the full process, ML models time to delivery drop dramatically. Another great benefit from MLOps is that the pipelines are designed to be reproducible, making it easy to re-use them for another project. 

One of the greatest benefits of MLOps is the potential for scalability. Due to the efficiency added to MLOps enhanced projects, scaling Machine Learning efforts across an enterprise becomes fairly easy. The world of Artificial Intelligence and Machine Learning is growing rapidly, and its applications in businesses are becoming more commoditized every day. That's why, to stay ahead of the pack, we need technology that enables us to deliver faster, and MLOps can do just that.

MLOps best practices

MLOps only adds value when using best practices. Here are two things to consider when developing an ML pipeline.

We already talked about the various teams of experts that have to work together when building and implementing an ML model. To get the best results, you need a hybrid team that covers all aspects of the project. Every step of the process should be well documented, to ensure everyone on the project knows what's going on.

To have the system perform consistently, testing and monitoring of machine learning is crucial. A feedback loop is essential to know if your model is doing well or if it's starting to drift. By evaluating the model with performance metrics, an informed decision can be made to maximize quality: keep the current model or deploy a new one.

Faktion's take on MLOps

Faktion has a lot of experience when it comes to building self-learning model pipelines. We’ve applied MLOps to bring hundreds of Automated Document Processing models to production for our clients as part of the Metamaze Platform, one of our spin-off companies. 

All these models need to be hosted at the same time on powerful GPU-enabled machines, which comes with a hefty price tag. However, our MLOps team came up with a clever solution. When a model doesn’t receive any incoming requests for a certain amount of time, our Kubernetes cluster will downscale the service freeing up resources for active models. This solution helped reduce hosting costs by 50%.

Conclusion

MLOps holds many promises for the future, yet it's already here. Its efficiency and scalability gains will become crucial to thriving in the business world of tomorrow. Faktion is right by your side to lead you on this path to success. Our expertise will help you accelerate your business towards a bright future!

Don't wa.i.t, get in touch with us today!