MLops with Kubeflow

Machine learning has been one of the most rapidly evolving technologies in recent times. As businesses realize the potential of machine learning models to gain valuable insights, they also need MLops strategies to streamline their entire deployment process and build robust pipelines for delivering machine learning models into production. This is where Kubeflow, an open-source ML platform that offers a complete solution for deploying and managing machine learning workflows on Kubernetes, comes into play.

Kubeflow helps organizations to manage their ML project lifecycle at scale, making it easier for teams to run experiments, deploy models and automate workflows on a cloud native platform. The Kubeflow project, which originated at Google, aims to simplify the deployment and orchestration of ML pipelines and workflows.

Kubeflow integrated in GCP Vertex AI

Google Cloud Platform’s Vertex AI is an end-to-end machine learning platform that you can use to build and deploy models. Vertex AI brings together several GCP services to create a cohesive platform, including AI Platform, Dataflow, and Kubeflow. Teams can deploy, manage and scale their machine learning models using Kubernetes, which is simplified using Kubeflow pipelines, and take advantage of the powerful hardware and auto-scaling features available through Vertex AI. Organizations can choose to use Vertex AI’s prebuilt models or can bring their own models to the platform. Vertex AI also provides data labeling services, model monitoring, and explanation tools, making it an…