Merge branch 'feature/readme-update' into develop

README.md

@@ -107,6 +107,8 @@ your project, you get the following benefits:
 
 ### 1. 💪 Write local, run anywhere
 
+<details>
+
 You only need to write your core machine learning workflow code once, but you
 can run it anywhere. We decouple your code from the environment and
 infrastructure on which this code runs.
@@ -120,8 +122,12 @@ CLI tool.
 ![You can run your pipelines locally or in the
 cloud](docs/book/assets/core_concepts/concepts-3.png)
 
+</details>
+
 ### 2. 🌈 All your MLOps stacks in one place
 
+<details>
+
 Once code is organized into a ZenML pipeline, you can supercharge your ML
 development with [powerful
 integrations](https://docs.zenml.io/features/integrations) on multiple [MLOps
@@ -138,8 +144,12 @@ to see how they work.
 
 ![ZenML is the glue](docs/book/assets/stack-list.png)
 
+</details>
+
 ### 3. 🛠 Extensibility
 
+<details>
+
 ZenML's Stack Components are built to support most machine learning use cases.
 We offer a batteries-included initial installation that should serve many needs
 and workflows, but if you need a special kind of monitoring tool added, for
@@ -148,8 +158,12 @@ framework making it easy to extend and build out whatever you need.
 
 ![ZenML is fully extensible](docs/book/assets/extensibility.gif)
 
+</details>
+
 ### 4. 🔍 Automated metadata tracking
 
+<details>
+
 ZenML tracks metadata for all the pipelines you run. This ensures that:
 
 - Code is versioned
@@ -163,9 +177,12 @@ blogpost](https://blog.zenml.io/caching-ml-pipelines/) to learn more!)
 
 ![Visualize your pipeline steps](docs/book/assets/dag-visualizer.png)
 
+</details>
 
 ### 5. ➿ Continuous Training and Continuous Deployment (CT/CD)
 
+<details>
+
 Continuous Training (CT) refers to the paradigm where a team deploys training pipelines 
 that run automatically to train models on new (fresh) data. Continuous Deployment (CD) 
 refers to the paradigm where newly trained models are automatically deployed to a prediction 
@@ -185,6 +202,8 @@ zenml served-models list
 
 Read more about CT/CD in ZenML [here](https://blog.zenml.io/ci-ct-cd-with-zenml/).
 
+</details>
+
 # 🤸 Getting Started
 
 ## 💾 Install ZenML
@@ -285,7 +304,7 @@ pipeline = mnist_pipeline(
 pipeline.run()
 ```
 
-### Get a guided tour with `zenml go`
+# :racehorse: Get a guided tour with `zenml go`
 
 For a slightly more in-depth introduction to ZenML, taught through Jupyter
 notebooks, install `zenml` via pip as described above and type:
@@ -297,7 +316,7 @@ zenml go
 This will spin up a Jupyter notebook that showcases the above example plus more
 on how to use and extend ZenML.
 
-### 👭 Collaborate with your team
+# 👭 Collaborate with your team
 
 ZenML is built to support teams working together. The underlying infrastructure
 on which your ML workflows run can be shared, as can the data, assets and
@@ -356,12 +375,16 @@ label](https://github.com/zenml-io/zenml/labels/good%20first%20issue). If you
 would like to contribute, please review our [Contributing
 Guide](CONTRIBUTING.md) for all relevant details.
 
+<details><summary>See Contributer Analytics</summary>
+
 <br>
 
 ![Repobeats analytics
 image](https://repobeats.axiom.co/api/embed/635c57b743efe649cadceba6a2e6a956663f96dd.svg
 "Repobeats analytics image")
 
+</details>
+
 # 🆘 Where to get help
 
 First point of call should be [our Slack group](https://zenml.io/slack-invite/).