Simplifying machine learning with Kubernetes containers

Machine learning techniques are increasingly being used to simplify problems in our everyday lives. Various industries use the technology and have already achieved major successes with it: self-driving cars, image recognition, translation, speech recognition, games and medicine too. No wonder the business world quickly warmed to the technology, given the new possibilities.

Building effective AI models and deploying them in operation remains complex and time-consuming even today. The technology requires large data sets to be uploaded and processed. The machine learning model then has to classify the data, for example to be able to recognise images. After that, the AI has to be «trained», which takes a particularly long time. Once the model has learned enough, it is finally implemented in an app that can then be used directly.

Many companies make life easier for data scientists or machine learning engineers by introducing various tools such as Kubeflow or Neptune, which can speed up the process. This already considerably reduces the number of steps required and accelerates app development.

This is where Kubernetes can further improve, simplify and accelerate app development: with Kubernetes as a Service, all computations required to train the machine learning model can be offloaded to a Kubernetes cluster. The data scientist or machine learning engineer only has to take care of data cleansing and the actual code. The rest is handled by a Kubernetes-based tool.

With Kubernetes as a Service from Netstream, you develop such apps directly in the Netstream Cloud and can easily deploy them too.

Benefits:

• Use computing power from the cloud
• Kubernetes saves you time and reduces complexity.
• Develop market-ready apps faster.
• Distribute and use resources more effectively.