website/www/site/content/en/blog/ml-resources.md - beam - Git at Google

 ---
 title:  "New Resources Available for Beam ML"
 date:   2022-11-09 00:00:01 -0800
 categories:
   - blog
   - python
 authors:
   - damccorm
 ---
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 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!

 To get started, we encourage you to visit Beam's new [AI/ML landing page](/documentation/ml/overview/).
 We've got plenty of content on things like [multi-model pipelines](/documentation/ml/multi-model-pipelines/),
 [performing inference with metrics](/documentation/ml/runinference-metrics/),
 [online training](/documentation/ml/online-clustering/), and much more.

 <img class="center-block"
      src="/images/blog/ml-landing.png"
      alt="ML landing page">

 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`.

 <img class="center-block"
      src="/images/blog/ensemble-model-notebook.png"
      alt="Example ensemble notebook with RunInference">

 Adding more examples and notebooks will be a point of emphasis going forward.
 For our next round of improvements, we are planning on adding examples of
 using RunInference with >30GB models, with multi-language pipelines, with
 common Beam concepts, and with TensorRT. We will also add examples showing
 other pieces of the Machine Learning lifecycle like model evaluation with TFMA,
 per-entity training, and more online training.

 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)!
	---
	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!

	To get started, we encourage you to visit Beam's new [AI/ML landing page](/documentation/ml/overview/).
	We've got plenty of content on things like [multi-model pipelines](/documentation/ml/multi-model-pipelines/),
	[performing inference with metrics](/documentation/ml/runinference-metrics/),
	[online training](/documentation/ml/online-clustering/), and much more.

	<img class="center-block"
	src="/images/blog/ml-landing.png"
	alt="ML landing page">

	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`.

	<img class="center-block"
	src="/images/blog/ensemble-model-notebook.png"
	alt="Example ensemble notebook with RunInference">

	Adding more examples and notebooks will be a point of emphasis going forward.
	For our next round of improvements, we are planning on adding examples of
	using RunInference with >30GB models, with multi-language pipelines, with
	common Beam concepts, and with TensorRT. We will also add examples showing
	other pieces of the Machine Learning lifecycle like model evaluation with TFMA,
	per-entity training, and more online training.

	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)!