Add automatic deployment and redeployment feature

Author: htahir1

README.md

@@ -75,6 +75,58 @@ export OPENPIPE_API_KEY=opk-your-api-key
 python run.py
 ```
 
+Once the pipeline completes, OpenPipe automatically deploys your fine-tuned model and makes it available through their API. You can immediately use your model with a simple API call:
+
+```bash
+curl https://api.openpipe.ai/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer opk-your-api-key" \
+  -d '{
+    "model": "customer_service_assistant",
+    "messages": [
+      {"role": "system", "content": "You are a helpful customer service assistant."},
+      {"role": "user", "content": "How do I reset my password?"}
+    ]
+  }'
+```
+
+For Python applications, you can use the OpenPipe Python SDK:
+
+```python
+# pip install openpipe
+
+from openpipe import OpenAI
+
+client = OpenAI(
+  openpipe={"api_key": "opk-your-api-key"}
+)
+
+completion = client.chat.completions.create(
+    model="openpipe:customer_service_assistant",
+    messages=[
+        {
+            "role": "system",
+            "content": "You are a helpful customer service assistant for Ultra electronics products."
+        },
+        {
+            "role": "user",
+            "content": "Can I trade in my old device for a new UltraPhone X?"
+        }
+    ],
+    temperature=0,
+    openpipe={
+        "tags": {
+            "prompt_id": "counting",
+            "any_key": "any_value"
+        }
+    },
+)
+
+print(completion.choices[0].message)
+```
+
+When you need to update your model with new data, simply run the pipeline again, and OpenPipe will automatically retrain and redeploy the updated model.
+
 ## ✨ Key Features
 
 ### 📊 End-to-End Fine-Tuning Pipeline
@@ -151,6 +203,20 @@ All metadata is accessible in the ZenML dashboard, enabling:
 - Easy reproduction of successful training jobs
 - Audit trails for model governance
 
+### 🚀 Automatic Deployment and Redeployment
+
+A key advantage of this integration is that OpenPipe automatically deploys your fine-tuned model as soon as training completes. Your model is immediately available via API without any additional deployment steps.
+
+![OpenPipe Deployed Model](zenml_openpipe_pipeline_deployed.png)
+*The OpenPipe console showing a successfully deployed fine-tuned model*
+
+When you run the pipeline again with new data, OpenPipe automatically retrains and redeploys your model, ensuring your production model always reflects your latest data. This makes it easy to implement a continuous improvement cycle:
+
+1. Fine-tune initial model
+2. Collect feedback and new examples
+3. Rerun the pipeline to update the model
+4. Repeat to continuously improve performance
+
 ## 📚 Advanced Usage
 
 ### Custom Data Source

assets/zenml_openpipe_pipeline_deployed.png

@@ -40,6 +40,8 @@ This integration enables data scientists and ML engineers to:
 3. **Deploy fine-tuned models to production** with confidence
 4. **Schedule recurring fine-tuning jobs** as data evolves
 
+A key advantage of this integration is that **OpenPipe automatically deploys your fine-tuned models** as soon as training completes, making them immediately available via API. When you run the pipeline again with new data, your model is automatically retrained and redeployed, ensuring your production model always reflects your latest data.
+
 ## Building a Fine-Tuning Pipeline
 
 Let's examine the core components of an LLM fine-tuning pipeline built with ZenML and OpenPipe.
@@ -217,6 +219,79 @@ python run.py \
 
 The implementation follows [OpenPipe's fine-tuning best practices](https://docs.openpipe.ai/) while leveraging [ZenML's orchestration capabilities](https://docs.zenml.io/stack-components/orchestrators).
 
+### Using Your Deployed Model
+
+Once the fine-tuning process completes, OpenPipe automatically deploys your model and makes it available through their API. You can immediately start using your fine-tuned model with a simple curl request:
+
+![OpenPipe Deployed Model](zenml_openpipe_pipeline_deployed.png)
+*The OpenPipe console showing a successfully deployed fine-tuned model*
+
+```bash
+curl https://api.openpipe.ai/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer opk-your-api-key" \
+  -d '{
+    "model": "rapidtech_support_assistant",
+    "messages": [
+      {"role": "system", "content": "You are a helpful customer support assistant for RapidTech products."},
+      {"role": "user", "content": "I need to reset my password for AccountManager"}
+    ],
+    "temperature": 0.7
+  }'
+```
+
+For Python applications, you can use the OpenPipe Python SDK, which follows the OpenAI SDK pattern for seamless integration:
+
+```python
+# pip install openpipe
+
+from openpipe import OpenAI
+
+client = OpenAI(
+  openpipe={"api_key": "opk-your-api-key"}
+)
+
+completion = client.chat.completions.create(
+    model="openpipe:rapidtech_support_assistant",
+    messages=[
+        {
+            "role": "system",
+            "content": "You are a helpful customer service assistant for RapidTech products."
+        },
+        {
+            "role": "user",
+            "content": "Can I trade in my old device for a new RapidTech Pro?"
+        }
+    ],
+    temperature=0,
+    openpipe={
+        "tags": {
+            "prompt_id": "customer_query",
+            "application": "support_portal"
+        }
+    },
+)
+
+print(completion.choices[0].message)
+```
+
+This SDK approach is particularly useful for integrating with existing applications or services, and it supports tagging your requests for analytics and monitoring.
+
+This immediate deployment capability eliminates the need for manual model deployment, allowing you to test and integrate your custom model right away.
+
+### Automated Redeployment with New Data
+
+When product information changes or you collect new training data, simply run the pipeline again:
+
+```bash
+python run.py \
+  --data-source=updated_support_conversations.csv \
+  --model-name=rapidtech_support_assistant \
+  --force-overwrite=True
+```
+
+OpenPipe will automatically retrain and redeploy your model with the updated data, ensuring your production model always reflects the latest information and examples. This seamless redeployment process makes it easy to keep your models up to date without manual intervention.
+
 ### Performance Metrics and Cost Analysis
 
 The fine-tuned model demonstrates:
@@ -319,6 +394,18 @@ This provides a real-time view of:
 - Error messages or warnings
 - Time spent in each training phase
 
+### Continuous Model Improvement
+
+A key advantage of the ZenML-OpenPipe integration is the ability to implement a continuous improvement cycle for your fine-tuned models:
+
+1. **Initial training**: Fine-tune a model on your current dataset
+2. **Production deployment**: Automatically handled by OpenPipe
+3. **Feedback collection**: Gather new examples and user interactions
+4. **Dataset augmentation**: Add new examples to your training data
+5. **Retraining and redeployment**: Run the pipeline again to update the model
+
+With each iteration, both the dataset and model quality improve, creating a virtuous cycle of continuous enhancement. Since OpenPipe automatically redeploys your model with each training run, new capabilities are immediately available in production without additional deployment steps.
+
 Check out [OpenPipe's model monitoring documentation](https://docs.openpipe.ai/features/fine-tuning/quick-start) for more information about monitoring your fine-tuned models in production.
 
 ### Deployment on ZenML Stacks
@@ -345,6 +432,8 @@ From implementing this integration with multiple customers, several key insights
 
 5. **Metadata tracking enables governance** – Complete lineage from data to model deployment satisfies compliance requirements.
 
+6. **Automatic deployment accelerates time-to-value** – With OpenPipe's instant deployment, fine-tuned models are immediately usable via API without additional DevOps work.
+
 ## Next Steps
 
 For teams looking to implement LLM fine-tuning in production, we recommend: