Learn how to use Kubeflow to set up machine learning orchestration on Kurbetenes for your AI projects.
In recent years, AI and Machine Learning have seen tremendous growth across industries in various innovative use cases. It is the most important strategic trend for business leaders. When we dive into a technology, the first step is usually experimentation on a small scale and for very basic use cases, then the next step is to scale up operations. Sophisticated ML models help companies efficiently discover patterns, uncover anomalies, make predictions and decisions, and generate insights, and are increasingly becoming a key differentiator in the marketplace. Companies recognise the need to move from proof of concepts to engineered solutions, and to move ML models from development to production. There is a lack of consistency in tools and the development and deployment process is inefficient. As these technologies mature, we need operational discipline and sophisticated workflows to take advantage and operate at scale. This is popularly known as MLOps or ML CI/ CD or ML DevOps. In this article, we explore how this can be achieved with the Kubeflow project, which makes deploying machine learning workflows on Kubernetes simple, portable, and scalable.
There are Enterprise ML platforms like Amazon SageMaker, Azure ML, Google Cloud AI, and IBM Watson Studio in public cloud environments. In case of on-prem and hybrid open source platform, the most notable project is Kubeflow.
Kubeflow is a curated collection of machine learning frameworks and tools. It is a platform for data scientists and ML engineers who want to experiment with their model and design an efficient workflow to develop, test and deploy at scale. It is a portable, scalable, and open-source platform built on top of Kubernetes by abstracting the underlying Kubernetes concepts.
Kubeflow utilizes various cloud native technologies like Istio, Knative, Argo, Tekton, and leverage Kubernetes primitives such as deployments, services, and custom resources. Istio and Knative help provide capabilities like blue/green deployments, traffic splitting, canary releases, and auto-scaling. Kubeflow abstracts the Kubernetes components by providing UI, CLI, and easy workflows that non-kubernetes users can use.
For the ML capabilities, Kubeflow integrates the best framework and tools such as TensorFlow, MXNet, Jupyter Notebooks, PyTorch, and Seldon Core. This integration provides data preparation, training, and serving capabilities.
#devops #kubernetes #tensorflow #kubeflow #machine-learning