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 @@
+ {% include "shared/_azure-banner.html" %}

Deliver AI at scale @@ -103,7 +104,7 @@

@@ -138,7 +139,7 @@

Full support for your ML stack

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+

Managed MLOps

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Managed MLOps

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Open source MLOps in the public cloud

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Open source MLOps in the public cloud

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Get your team up-to-date @@ -200,7 +201,7 @@

MLOps resources

One of our customers migrated from a legacy platform to Canonical MLOps and reduced their operational costs.

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MLOps resources

Learn to take models to production using open source MLOps platforms.

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MLOps resources

Find out how to streamline operations and scale AI initiatives using open source MLOps platforms on NVIDIA DGX.

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MLOps resources

Run open source MLOps on AWS to remove compute power constraints and start your AI project quickly.

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+

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 %} +

+
+ {% include "shared/_azure-banner.html" %} +
+
@@ -35,7 +40,7 @@

What is Kubeflow?

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Try out Charmed Kubeflow Read our MLOps toolkit
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What's inside Kubeflow?

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Kubeflow dashboard

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ML libraries & frameworks

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Kubeflow Pipelines

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Hyperparameter tuning / AutoML

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.

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+

KServe for inference serving

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MLOps at any scale

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.

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+
Get the whitepaper
@@ -206,7 +211,7 @@

Why MLOps?

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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Read more about MLOps
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Who uses Kubeflow?

Thousands of companies have chosen Kubeflow for their AI/ML stack.

From research institutions like CERN, to transport and logistics companies - Uber, Lyft, GoJek - to financial and media industries with Spotify, Bloomberg, Shopify and PayPal.

Forward-looking enterprises are using Kubeflow to empower their data scientists.

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Read our case study with the University of Tasmania
diff --git a/templates/azure/index.html b/templates/azure/index.html index 7018e052e4f..cdfcdceb7db 100644 --- a/templates/azure/index.html +++ b/templates/azure/index.html @@ -15,6 +15,11 @@ {% endblock body_class %} {% block content %} +
+
+ {% include "shared/_azure-banner.html" %} +
+
diff --git a/templates/shared/_azure-banner.html b/templates/shared/_azure-banner.html new file mode 100644 index 00000000000..e20901fd3ff --- /dev/null +++ b/templates/shared/_azure-banner.html @@ -0,0 +1,12 @@ +
+
+
+ Apply to join our preview for Managed Kubeflow on Azure! +
+

+ + Learn more and sign up. + +

+
+