Kubeflow is a powerful and flexible MLOps platform . By using Kubeflow as the foundation of your MLOps platform, you can ensure the lifecycle of your ML projects are : uniformly managed, experiments are reproducible, prototyping is quick and you can ship your models into production without data scientists needing deep expertise y in infrastructure. We also saw how Kubeflow Pipelines enables developers to build custom ML workflows by easily “stitching” and connecting various components like building blocks using containerized implementations of ML tasks providing portability, repeatability and encapsulation.. Kubeflow makes it easy to deploy your machine learning models whether you’re running your code as notebooks or in Docker containers, Kubeflow allows you to focus on the model not the infrastructure.. Kubeflow also lowers the barrier to entry by providing a visual representation of the pipeline making it easy to understand and adopt for new users and provides an interactive UI to look at metrics and compare runs.
When Kubeflow is paired with Atrikko’s tools like Kale and Rok, data scientists can further focus on building their models and run experimentations while the heavy lifting of creating the docker containers, building the kubeflow pipeline, running hyperparameter tuning and deploying. With the models deployed using Kale and Rok, it ensures the reproducibility of experiments by storing a snapshot of the data, code and all other artifacts related to a run, which can be later used to replicate an experiment.
In this session we'll go over what an MLOps platform promises, how to deploy and Kubeflow in AWS ecosystem and ensure AI/ML governance.