diff --git a/README.md b/README.md
index 489257a41c..ed4808b409 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,7 @@
-
Connecting data science teams seamlessly to cloud infrastructure.
-
+
Beyond The Demo: Production-Grade AI Systems
+
ZenML brings battle-tested MLOps practices to your AI applications, handling evaluation, monitoring, and deployment at scale
@@ -100,40 +100,44 @@ Take a tour with the guided quickstart by running:
zenml go
```
-## ๐ช Simple, integrated, End-to-end MLOps
+## ๐ช From Prototype to Production: AI Made Simple
-### Create machine learning pipelines with minimal code changes
+### Create AI pipelines with minimal code changes
-ZenML is a MLOps framework intended for data scientists or ML engineers looking to standardize machine learning practices. Just add `@step` and `@pipeline` to your existing Python functions to get going. Here is a toy example:
+ZenML is an open-source framework that handles MLOps and LLMOps for engineers scaling AI beyond prototypes. Automate evaluation loops, track performance, and deploy updates across 100s of pipelinesโall while your RAG apps run like clockwork.
```python
from zenml import pipeline, step
-@step # Just add this decorator
-def load_data() -> dict:
- training_data = [[1, 2], [3, 4], [5, 6]]
- labels = [0, 1, 0]
- return {'features': training_data, 'labels': labels}
+@step
+def load_rag_documents() -> dict:
+ # Load and chunk documents for RAG pipeline
+ documents = extract_web_content(url="https://www.zenml.io/")
+ return {"chunks": chunk_documents(documents)}
@step
-def train_model(data: dict) -> None:
- total_features = sum(map(sum, data['features']))
- total_labels = sum(data['labels'])
-
- print(f"Trained model using {len(data['features'])} data points. "
- f"Feature sum is {total_features}, label sum is {total_labels}")
+def generate_embeddings(data: dict) -> None:
+ # Generate embeddings for RAG pipeline
+ embeddings = embed_documents(data['chunks'])
+ return {"embeddings": embeddings}
-@pipeline # This function combines steps together
-def simple_ml_pipeline():
- dataset = load_data()
- train_model(dataset)
+@step
+def index_generator(
+ embeddings: dict,
+) -> str:
+ # Generate index for RAG pipeline
+ index = create_index(embeddings)
+ return index.id
+
-if __name__ == "__main__":
- run = simple_ml_pipeline() # call this to run the pipeline
-
+@pipeline
+def rag_pipeline() -> str:
+ documents = load_rag_documents()
+ embeddings = generate_embeddings(documents)
+ index = index_generator(embeddings)
+ return index
```
-
-
+
### Easily provision an MLOps stack or reuse your existing infrastructure
@@ -185,18 +189,47 @@ def training(...):
Create a complete lineage of who, where, and what data and models are produced.
-Youโll be able to find out who produced which model, at what time, with which data, and on which version of the code. This guarantees full reproducibility and auditability.
+You'll be able to find out who produced which model, at what time, with which data, and on which version of the code. This guarantees full reproducibility and auditability.
```python
from zenml import Model
-@step(model=Model(name="classification"))
-def trainer(training_df: pd.DataFrame) -> Annotated["model", torch.nn.Module]:
- ...
+@step(model=Model(name="rag_llm", tags=["staging"]))
+def deploy_rag(index_id: str) -> str:
+ deployment_id = deploy_to_endpoint(index_id)
+ return deployment_id
```
+## ๐ Key LLMOps Capabilities
+
+### Continual RAG Improvement
+**Build production-ready retrieval systems**
+
+
+
+
+
+