mlstacks blog announcement (#170)

* add starter * update with outline * update with image assets * finish draft * update with reference to 0.44.0 * british (correct) spelling -> American spelling * Optimised images with calibre/image-actions * update blog and add image * Optimised images with calibre/image-actions * update blog date and release links --------- Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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diff --git a/...3-09-01-introducing-mlstacks:-a-refreshed-way-to-deploy-mlops-infrastructure.md b/...3-09-01-introducing-mlstacks:-a-refreshed-way-to-deploy-mlops-infrastructure.md
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+---
+layout: post
+author: "Alex Strick van Linschoten"
+title: "Introducing mlstacks: a refreshed way to deploy MLOps infrastructure"
+description: "Introducing mlstacks: a refreshed way to deploy MLOps infrastructure"
+category: zenml
+tags: mlstacks production mlops zenml evergreen tooling
+publish_date: September 1, 2023
+date: 2023-09-01T08:05:51Z
+thumbnail: /assets/posts/mlstacks/mlstacks_logo.png
+image:
+  path: /assets/posts/mlstacks/mlstacks_logo.png
+---
+**Last updated:** September 1, 2023
+
+We're excited to launch [the new `mlstacks` Python package](https://mlstacks.zenml.io). MLStacks builds on
+the work done to create [Stack Recipes](https://github.com/zenml-io/mlstacks/tree/release/0.6.1#readme), allowing you to quickly spin up MLOps
+infrastructure using Terraform. It is designed to be used with ZenML, but can be
+used with any MLOps tool or platform.
+
+You can use `mlstacks` [directly as a Python package](https://pypi.org/project/mlstacks/), deploying infrastructure
+with the CLI after defining your stacks in YAML. Alternatively, you can let
+ZenML handle writing the stack and component specification files by [using the
+ZenML CLI](https://docs.zenml.io/stacks-and-components/stack-deployment) to define which components you want to deploy. We currently support
+modular MLOps stacks on AWS, GCP and K3D (for local use).
+
+We reworked the previous way of doing things to be more stable and reliable. We
+even added new features like the ability to get a cost estimate for your MLOps
+stacks. 
+
+![](/assets/posts/mlstacks/mlstacks_breakdown.png)
+
+We were inspired by [the work done by the FuzzyLabs team on Matcha](https://github.com/fuzzylabs/matcha) as well as by
+conversations with users of the old `mlops-stacks` repository / functionality.
+In that spirit, MLStacks is also designed to be able to work as a standalone deployment
+option independent of ZenML. All of this is available in the new `mlstacks`
+Python package and with [ZenML's latest release (0.44.1)](https://github.com/zenml-io/zenml/releases/tag/0.44.1).
+
+## How can I try it out?
+
+To use `mlstacks` directly you'll want to create .yaml files to define your
+stack and the individual components in your stack. (Examples of how to do this
+can be found in [the dedicated `mlstacks` documentation
+site.](https://mlstacks.zenml.io/getting-started/quickstart))
+
+<div style="position: relative; padding-bottom: 65.0994575045208%; height: 0;"><iframe src="https://www.loom.com/embed/1a37379a5e1c463d914041b9124afa78?sid=feaf1422-708a-442c-9cd6-a6814858d4d9" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style="position: absolute; top: 0; left: 0; width: 100%; height: 100%;"></iframe></div>
+
+Using `mlstacks` with ZenML is easier as you aren't required to define your
+stacks and components in .yaml files; ZenML handles all of that for you. As a
+quick example, you can run:
+
+```bash
+zenml stack deploy -p gcp -a -n my_mlstacks_stack -r us-east1 -t env=dev -x bucket_name=my-new-bucket -x project_id=zenml
+```
+
+This would deploy a stack to GCP that has an artifact store created for you in
+the `us-east1` region. It will also import that stack (with the name
+`my_mlstacks_stack`) into ZenML ready for you to use.
+
+<div style="position: relative; padding-bottom: 65.0994575045208%; height: 0;"><iframe src="https://www.loom.com/embed/cf73550229ce488eba6c071b7c61b1f4?sid=2d428b25-8e8d-4711-8c24-a6c6c1292b54" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style="position: absolute; top: 0; left: 0; width: 100%; height: 100%;"></iframe></div>
+
+Most of the examples shown here are fairly simple as they allow you to try
+things out without needing to wait for too long or to incur significant cloud
+costs, but you're encouraged to try it out for stacks that suit your needs!
+
+## How it works
+
+<div style="position: relative; padding-bottom: 89.99999999999999%; height: 0;"><iframe src="https://www.loom.com/embed/4fd5c428728b4f729234cb4c96f8d5a5?sid=cf73a6c1-b099-4723-8e19-31ff3f09625f" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style="position: absolute; top: 0; left: 0; width: 100%; height: 100%;"></iframe></div>
+
+MLStacks works by taking stack and component specification files and parsing
+them into Terraform files which are in turn deployed to your cloud (or local, in
+the case of `k3d`) infrastructure. Our Python package will validate your
+specification files to ensure that you're not using invalid or unsupported
+combinations of components.
+
+![](/assets/posts/mlstacks/mlstacks.jpg)
+
+If you find something that we don't yet support but that you need for your work,
+we encourage you to contribute to the project!
+
+When using `mlstacks` through ZenML, you don't have to do the work of writing
+the stack specification .yaml files. You simply need to compose the CLI command
+that specifies which components you need in your stack.
+
+![](/assets/posts/mlstacks/zenml-with-mlstacks.jpg)
+
+We're working to provide other (easier) ways to deploy your infrastructure using
+ZenML but for now CLI deployments offer all the flexibility of the `mlstacks`
+package without the need to write your own stack and component specifications!
+
+## Try it out!
+
+We encourage you to try out `mlstacks` and to deploy MLOps stacks, either
+through ZenML or by writing your own stack specifications. Full guides for both
+options are available in our documentation:
+
+- To spin up infrastructure using ZenML, [start with our
+  introduction](https://docs.zenml.io/stacks-and-components/stack-deployment/deploy-a-stack-component)
+  to deploying individual stack components via the CLI
+- To try out `mlstacks` directly and see how easy it is to specify your stacks
+  using the .yaml specification files, [check out one of our QuickStart
+  guides](https://mlstacks.zenml.io/getting-started/quickstart) appropriate to
+  your favorite cloud platform (or use the `k3d` guide to test deployment
+  locally)
+
+We'd love to hear your feedback, good and bad! Please [let us
+know](https://zenml.io/slack-invite) how you got on with `mlstacks` [in
+Slack](https://zenml.io/slack-invite). Happy deploying!
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