Handle MLOps across multiple cloud providers using Kubeflow
Machine Learning Models are relatively easy to build but hard to roll out. Learn how to make ML workflows production-ready with Kubeflow.
Cloud Collective is a meetup for technologists interested in the Cloud Native approach towards developing and deploying web & mobile applications, streaming analytics, ETL pipelines, microservices, containers, functions, and applying Infrastructure as Code principles for automating infrastructure on AWS, Azure, and Google Cloud.
With the recent huge demand and traction of DevOps and GitOps, many organizations are struggling to apply those practices to ML workloads. That's the exact use case we wanted to talk about in this meetup.
Quickly Deploy ML Workloads on Multi-Cloud Using Kubeflow. Learn how to quickly produce an end-to-end AI pipeline and easily deploy ML workloads onto multi-cloud using the well-known open-source platform called Kubeflow Pipelines. All major components of the AI pipeline such as data pre-processing, hyperparameter tuning, model training, model prediction, model explanation, and training orchestration can be easily implemented on the cloud with just a few easy steps.
If you enjoyed this talk, join our cloud collective meetup here ► https://www.meetup.com/cloud-collective/ and participate in our next meetup!
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