Katib: It's like having a chef's assistant who helps you find the best recipe for your cookies automatically. Notebooks: These are like interactive cookbooks. Kubeflow helps you create and run these recipes for your ML tasks. Pipelines: Picture a recipe that tells you how to make cookies step by step. AutoML □: Want a machine to learn how to bake cakes on its own? Kubeflow can help you with that, too! Common Terminologies Kubeflow helps you do that efficiently.Ģ. Distributed Training □: Imagine baking a giant cake together with other bakers. Kubeflow schedules when and how the cakes get baked. Orchestration □: It's like a conductor coordinating an orchestra. Monitoring □: Kubeflow keeps an eye on all your bakeries, making sure they're all baking cakes perfectly.Ĥ. Scaling □: When you need to make lots of cakes at once, Kubeflow helps you set up more ovens (computers) to bake them faster.ģ. Kubeflow puts your cake in a container (like a box) that's easy to move around.Ģ. Containerization □: Think of your cake as a package. Kubeflow is like the magic that helps all your bakeries work together smoothly. ![]() Imagine you're running a bakery chain with multiple stores, each making cakes. Model Serving: This is where you take your model from the museum and put it to work, like hiring a superhero for a job. Model Registry: It's like a museum where you keep your best models on display. It tells you how to cook the same ML experiment again and again. Projects: Think of this as a recipe card. You write down what you did, what worked, and what didn't. Experiment Tracking: It's like a journal for experiments. Model Versioning □: Just like releasing a new edition of a recipe book, MLFlow keeps track of different versions of your models. MLFlow makes it easy to serve your model like a chef serves their best cake.ģ. Model Deployment □: Once your cake is perfect, you want to share it with the world. It automatically adjusts the "ingredients" to make your model perform better.Ģ. Hyperparameter Tuning □️: MLFlow helps you figure out the perfect amount of sugar for your cake. In the ML world, you share your models and results with your team. Sharing Results □: Share your cake recipes with your friends. Reproducibility □: Ever made a cake that tasted amazing but couldn't remember the exact recipe? MLFlow ensures you can recreate the same delicious cake every time!Ĥ. MLFlow helps you save and organize them.ģ. ![]() Managing Models □: Think of your models as different cake recipes. MLFlow helps you compare different "bakes" or experiments.Ģ. Tracking Experiments □: When you bake a cake multiple times, you want to see which one tastes the best. □ It helps you keep track of all the ingredients and steps. MLFlow is like a super organized recipe book for your machine learning projects.
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