[beam] branch users/damccorm/ml-resources-blog created (now b37244e69cd)

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      at b37244e69cd Create ml-resources.md

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[beam] 01/01: Create ml-resources.md

[da...@apache.org]

This is an automated email from the ASF dual-hosted git repository.

damccorm pushed a commit to branch users/damccorm/ml-resources-blog
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commit b37244e69cd6eb75b482ef6d2aea30bb25902466
Author: Danny McCormick <da...@google.com>
AuthorDate: Wed Nov 9 15:53:51 2022 -0500

    Create ml-resources.md
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+---
+title:  "New Resources Available for Beam ML"
+date:   2022-11-09 00:00:01 -0800
+categories:
+  - blog
+  - python
+authors:
+  - damccorm
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+If you've been paying attention, over the past year you've noticed that
+Beam has released a number of features designed to make Machine Learning
+easy. Ranging from things like the introduction of the `RunInference`
+transform to the continued refining of `Beam Dataframes`, this has been
+an area where we've seen Beam make huge strides. While development has
+advanced quickly, however, until recently there has been a lack of
+resources to help people discover and use these new features.
+
+Over the past several months, we've been hard at work building out
+documentation and notebooks to make it easier to use these new features
+and to show how Beam can be used to solve common Machine Learning problems.
+We're now happy to present this new and improved Beam ML experience!
+
+TODO - add image of notebook
+
+To get started, we encourage you to visit Beam's new [AI/ML landing page](https://beam.apache.org/documentation/ml/overview/).
+We've got plenty of content on things like [multi-model pipelines](https://beam.apache.org/documentation/ml/multi-model-pipelines/),
+[performing inference with metrics](https://beam.apache.org/documentation/ml/runinference-metrics/),
+[online training](https://beam.apache.org/documentation/ml/online-clustering/), and much more.
+
+We've also introduced a number of example [Jupyter Notebooks](https://github.com/apache/beam/tree/master/examples/notebooks/beam-ml)
+showing how to use built in beam transforms like `RunInference` and `Beam Dataframes`.
+
+We hope you find this useful! As always, if you see any areas for improvement, please [open an issue](https://github.com/apache/beam/issues/new/choose)
+or a [pull request](https://github.com/apache/beam/pulls)!