Machine Learning at the Edge, how to deploy it successfully?

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: agility as a cornerstone for machine learning growth

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.

Challenges to the Adoption of MLOps Philosophy in Machine Learning Engineering

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:

  1. Boards of directors do not always see Machine Learning as a strategic point of the company and perceive this type of project as something difficult to measure and manage.
  2. Machine Learning initiatives often work in isolation from each other, making it difficult to integrate processes across teams.
  3. To achieve efficiency, model training requires large amounts of quality data, which generates significant costs in data accessibility, preparation and management.
  4. Data science involves a lot of trial and error, which makes it difficult to plan a project in time.
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.

IoT Edge MLOps Challenges

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.

Barbara Platform as an enabler for Edge MLOps

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: 

  • Increase productivity and improve operations by reducing variations in model iterations for industrial-grade scenarios, using reproducible models and combining them to create models that can be automatically retrained for IoT Edge devices.
  • Automatically scale and deploy applications without code, automating the processes of compiling and deploying Machine Learning models for perimeter devices.
  • Easily and rapidly deploy highly accurate and reliable models anywhere, packaging models quickly using Dockers and releasing them in a controlled manner to production.
  • Effectively manage the entire machine learning lifecycle, benefiting from the interoperability of the platform.

Implementing Machine Learning models at the Edge poses some challenges that Barbara also can help with: 

  • IoT Machine Learning models change rapidly. Therefore, they need more frequent and automatic re-training, our Barbara Edge Orchestrator section allows you to download new algorithms to the Edge Node or upgrade existing models to newer versions.
  • IoT Machine Learning models typically rely on a wide variety of devices, with different technologies. Our extensive connector library allows Edge Nodes to connect to any sensor, actuator, PLC or industrial equipment to exchange information and commands.
  • IoT Edge solutions may often need to run in different connection environments, so different connectivity standards need to be enabled. Barbara OS can work with the network that best suits your coverage, battery consumption and bandwidth needs. Barbara OS offers connectivity through short-range technologies such as WiFi or Zigbee and long-range standards such as 5G. It can also integrate with LPWAN-type networks such as LoRaWan.


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.