We are still at the dawn of Machine Learning and Artificial Intelligence in the Industry. Still, as we envision new use cases and develop them in our environment, we realise success in the future depends on the proper implementation today.
MLOps (DevOps for machine learning) enables data science, and IT teams to collaborate and increase the pace of model development and deployment via monitoring, validation, and governance of machine learning models.
Automating and operationalising ML products is challenging. Many ML endeavours fail to deliver on their expectations. The paradigm of Machine Learning Operations (MLOps) addresses this issue.
Machine learning operations (MLOps) is similar to developer operations (DevOps), but with a focus on deploying, maintaining and retraining machine learning models rather than code versioning and software.
MLOps, like DevOps, increases ML teams' agility by making it possible to quickly and frequently introduce small, incremental changes that help maintain the reliability of machine learning models. Importantly, MLOps also allows teams of IT professionals to deal with model deployment and maintenance, so data scientists can spend their time on model development.
In this sense, projects that apply DevOps philosophy in the development, deployment and maintenance of AI algorithms will prosper more likely.
MLOps is a core function of Machine Learning engineering, focused on streamlining the process of taking machine learning models to production, and then maintaining and monitoring them.
However, the adoption of the MLOps philosophy faces several challenges:
MLOps is about breaking away from slow and linear practices, to transform development processes into the rapid continuous iteration, allowing developers to constantly create and deploy innovative solutions.
The deployment of machine learning models in production presents one of the most significant pain points in the workflow. The deployment process presents additional challenges when the target platform is IoT Edge.
IoT machine learning models are rapidly changing hence, they degrade faster (concerning data drift of the current data). Therefore, they need more frequent and automatic retraining.
IoT machine learning models need to be deployed on different kinds of target platforms, and you need to leverage the capabilities of these platforms in terms of performance, security, and so on.
IoT Edge solutions may need to run offline – hence you need to allow for offline working with the frequency of refresh for the models.
In an Industry with more and more distributed edge nodes, executing more complex AI-based algorithms demands an Edge Infrastructure designed to maintain the lifecycle of trained models and the IoT devices that run them.
In this data scientist-led process, Barbara´s Edge platform facilitates data scientists to deploy, start, monitor, stop or update applications and models to thousands of distributed edge nodes.
With Barbara Edge Orchestrator, they can collaborate and increase the pace of model development and deployment by monitoring, validating and governing Machine Learning models.
With Barbara´s Edge Platform, they can:
Implementing Machine Learning models at the Edge poses some challenges that Barbara also can help with:
The competitiveness of industries in the future will be defined by Machine Learning and MLOps. Companies that remain at a lower level will be at a significant disadvantage to those that are in a position to expand their ML efforts to provide a real business advantage.
The importance of agility in technology development is even more important in business decisions. Budgets rarely arise specifically to improve process maturity. The business technology leader is in a unique position to take the initiative and create projects that use machine learning as a catalyst for the company.
If you like to learn more about how to implement MLOps at the Edge get in touch.