Update README.md

README.md

@@ -12,10 +12,10 @@
 ______________________________________________________________________
 
 <p align="center">
-  <a href="https://lightning.ai/">Lightning.ai</a> •
+  <a href="https://lightning.ai/">Lightning AI</a> •   
+  <a href="#examples">Examples</a> •
   <a href="https://lightning.ai/docs/pytorch/stable/">PyTorch Lightning</a> •
   <a href="https://lightning.ai/docs/fabric/stable/">Fabric</a> •
-  <a href="https://lightning.ai/docs/app/stable/">Lightning Apps</a> •
   <a href="https://pytorch-lightning.readthedocs.io/en/stable/">Docs</a> •
   <a href="#community">Community</a> •
   <a href="https://lightning.ai/docs/pytorch/stable/generated/CONTRIBUTING.html">Contribute</a> •
@@ -92,23 +92,21 @@ pip install -iU https://test.pypi.org/simple/ pytorch-lightning
 
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-## Lightning has 4 core packages
+## Lightning has 2 core packages
 
 [PyTorch Lightning: Train and deploy PyTorch at scale](#pytorch-lightning-train-and-deploy-pytorch-at-scale).
 <br/>
 [Lightning Fabric: Expert control](#lightning-fabric-expert-control).
-<br/>
-[Lightning Data: Blazing fast, distributed streaming of training data from cloud storage](https://github.com/Lightning-AI/pytorch-lightning/tree/master/src/lightning/data).
-<br/>
-[Lightning Apps: Build AI products and ML workflows](#lightning-apps-build-ai-products-and-ml-workflows).
 
 Lightning gives you granular control over how much abstraction you want to add over PyTorch.
 
 <div align="center">
     <img src="https://pl-public-data.s3.amazonaws.com/assets_lightning/continuum.png" width="80%">
 </div>
 
-______________________________________________________________________
+&nbsp;
+&nbsp;
+
 
 # PyTorch Lightning: Train and Deploy PyTorch at Scale
 
@@ -118,6 +116,15 @@ PyTorch Lightning is just organized PyTorch - Lightning disentangles PyTorch cod
 
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+### Examples
+Explore various types of training possible with PyTorch Lightning. Pretrain and finetune ANY kind of model to perform ANY task like classification, segmentation, summarization and more:    
+
+| Task  | Description | Run |
+|---|---|---|
+| [Hello world](#hello-simple-model)  | Pretrain - Hello world example | <a target="_blank" href="https://lightning.ai/lightning-ai/studios/pytorch-lightning-hello-world"><img src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/app-2/studio-badge.svg" alt="Open In Studio"/></a> |
+| [Image segmentation](https://lightning.ai/lightning-ai/studios/image-segmentation-with-pytorch-lightning)  | Finetune - ResNet-50 model to segment images | <a target="_blank" href="https://lightning.ai/lightning-ai/studios/image-segmentation-with-pytorch-lightning"><img src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/app-2/studio-badge.svg" alt="Open In Studio"/></a> |   
+| [Text classification](https://lightning.ai/lightning-ai/studios/text-classification-with-pytorch-lightning)  | Finetune - text classifier (BERT model) | <a target="_blank" href="https://lightning.ai/lightning-ai/studios/text-classification-with-pytorch-lightning"><img src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/app-2/studio-badge.svg" alt="Open In Studio"/></a> |   
+
 ### Hello simple model
 
 ```python
@@ -319,6 +326,9 @@ ______________________________________________________________________
 
 ______________________________________________________________________
 
+&nbsp;
+&nbsp;
+
 # Lightning Fabric: Expert control.
 
 Run on any device at any scale with expert-level control over PyTorch training loop and scaling strategy. You can even write your own Trainer.
@@ -501,62 +511,8 @@ ______________________________________________________________________
 
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-# Lightning Apps: Build AI products and ML workflows
-
-Lightning Apps remove the cloud infrastructure boilerplate so you can focus on solving the research or business problems. Lightning Apps can run on the Lightning Cloud, your own cluster or a private cloud.
-
-<div align="center">
-    <img src="https://pl-public-data.s3.amazonaws.com/assets_lightning/lightning-apps-teaser.png" width="80%">
-</div>
-
-## Hello Lightning app world
-
-```python
-# app.py
-import lightning as L
-
-
-class TrainComponent(L.LightningWork):
-    def run(self, x):
-        print(f"train a model on {x}")
-
-
-class AnalyzeComponent(L.LightningWork):
-    def run(self, x):
-        print(f"analyze model on {x}")
-
-
-class WorkflowOrchestrator(L.LightningFlow):
-    def __init__(self) -> None:
-        super().__init__()
-        self.train = TrainComponent(cloud_compute=L.CloudCompute("cpu"))
-        self.analyze = AnalyzeComponent(cloud_compute=L.CloudCompute("gpu"))
-
-    def run(self):
-        self.train.run("CPU machine 1")
-        self.analyze.run("GPU machine 2")
-
-
-app = L.LightningApp(WorkflowOrchestrator())
-```
-
-Run on the cloud or locally
-
-```bash
-# run on the cloud
-lightning run app app.py --setup --cloud
-
-# run locally
-lightning run app app.py
-```
-
-______________________________________________________________________
-
-<div align="center">
-    <a href="https://lightning.ai/docs/app/stable/">Read the Lightning Apps docs</a>
-</div>
-
-______________________________________________________________________
+&nbsp;
+&nbsp;
 
 ## Examples
 
@@ -587,7 +543,8 @@ ______________________________________________________________________
 - [Logistic Regression](https://lightning-bolts.readthedocs.io/en/stable/models/classic_ml.html#logistic-regression)
 - [Linear Regression](https://lightning-bolts.readthedocs.io/en/stable/models/classic_ml.html#linear-regression)
 
-______________________________________________________________________
+&nbsp;
+&nbsp;
 
 ## Continuous Integration
 
@@ -611,7 +568,8 @@ Lightning is rigorously tested across multiple CPUs, GPUs and TPUs and against m
 </center>
 </details>
 
-______________________________________________________________________
+&nbsp;
+&nbsp;
 
 ## Community