diff --git a/templates/ai/mlops.html b/templates/ai/mlops.html index ec28af55524..1edd7084419 100644 --- a/templates/ai/mlops.html +++ b/templates/ai/mlops.html @@ -20,6 +20,7 @@
One of our customers migrated from a legacy platform to Canonical MLOps and reduced their operational costs.
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Learn to take models to production using open source MLOps platforms.
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Find out how to streamline operations and scale AI initiatives using open source MLOps platforms on NVIDIA DGX.
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Run open source MLOps on AWS to remove compute power constraints and start your AI project quickly.
diff --git a/templates/ai/what-is-kubeflow.html b/templates/ai/what-is-kubeflow.html index 408aa5c531d..c1d64b75c3e 100644 --- a/templates/ai/what-is-kubeflow.html +++ b/templates/ai/what-is-kubeflow.html @@ -7,6 +7,11 @@ {% block body_class %}is-paper{% endblock body_class %} {% block content %} +
Kubeflow includes Katib for hyperparameter tuning. Katib runs pipelines with different hyperparameters (e.g. learning rate, # of hidden layers) optimising for the best ML model.
Enterprise-ready Charmed Kubeflow, the fully supported MLOps platform for any cloud, is validate and certified on high-end AI hardware, such as NVIDIA DGX.
A complete solution for sophisticated data science labs. Upgrades and security updates - all supported in the free, open source distribution.
-Bringing AI solutions to market can involve many steps: data pre-processing, training, model deployment or inference serving at scale... The list of tasks is complex and keeping them in a set of notebooks or scripts is hard to maintain, share and collaborate on, leading to inefficient processes.
Google describes that only about 20% of the effort and code required to bring AI systems to production is the development of ML code, while the remaining is operations. Standardizing ops in your ML workflows can hence greatly decrease time-to-market and costs for your AI solutions.
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