jupyter blog post

Signed-off-by: Daniel Lok <[email protected]>

website/blog/2024-12-20-mlflow-tracing-in-jupyter/index.md

@@ -0,0 +1,82 @@
+---
+title: MLflow Tracing in Jupyter Notebooks
+description: Introducing MLflow Tracing's Jupyter integration
+slug: mlflow-tracing-in-jupyter
+authors: [daniel-lok]
+tags: [genai, mlops, tracing]
+thumbnail: /img/blog/mlflow-tracing-in-jupyter.png
+---
+
+![Thumbnail](mlflow-tracing-in-jupyter-title.png)
+
+🚀 We're excited to announce a major upgrade to the [MLflow Tracing](https://mlflow.org/docs/latest/llms/tracing/index.html)
+experience!
+
+If you're not familiar with MLflow Tracing, it's an observability tool that allows you record the inputs and
+outputs of intermediate function executions. It's particularly useful in debugging GenAI applications, and MLflow has over
+a [dozen integrations with popular GenAI frameworks](https://mlflow.org/docs/latest/llms/tracing/index.html#automatic-tracing)
+to automatically generate traces without requiring you to change your existing code.
+
+As of **MLflow 2.20**, you can now view the MLflow Trace UI directly within Jupyter notebooks, allowing
+you to debug your applications without having to tab out of your development environment. Context
+switching can often be disruptive to one's workflow, and this feature makes it easier to stay focused while
+still being able to visualize the trace data that you generate.
+
+<figure>
+  ![MLflow Trace UI in Jupyter Notebook](jupyter-trace-ui.png)
+  <figcaption style={{ textAlign: "center" }}>An example of the UI in JupyterLab</figcaption>
+</figure>
+
+## Getting Started
+
+To get started, you'll need to be using an [MLflow Tracking Server](https://mlflow.org/docs/latest/tracking/server.html).
+Under the hood, the MLflow client needs to make network requests in order to fetch the UI assets and trace data.
+
+If you don't use a remote server, you can always start one locally by running the `mlflow server`
+[CLI command](https://mlflow.org/docs/latest/tracking/server.html#start-the-tracking-server). By default,
+the server will start up at `http://localhost:5000`.
+
+In your notebook, simply ensure that the MLflow Tracking URI is set to your tracking server, and you're good to go!
+
+```python
+import mlflow
+
+# replace this with your own URI, if it differs
+tracking_uri = "http://localhost:5000"
+mlflow.set_tracking_uri(tracking_uri)
+
+# create a new experiment to avoid cluttering the default experiment
+experiment = mlflow.set_experiment("mlflow-trace-ui-demo")
+
+# the trace UI should now show up whenever traces are generated,
+# for example:
+@mlflow.trace
+def add(a, b):
+  return a + b
+
+# running the traced function triggers the UI display
+add(1, 2)
+```
+
+The trace UI will show up whenever any of the following events happen:
+
+1. A trace is generated in the cell (via automatic tracing, or when running manually traced functions)
+2. When a trace object is explicitly displayed (e.g. via IPython's `display()` function)
+3. When the `mlflow.search_traces()` API is called
+
+For a hands-on experience with this feature, please try running our
+[**demo notebook**](https://github.com/mlflow/mlflow/blob/master/docs/source/llms/tracing/notebooks/jupyter-trace-demo.ipynb)!
+The notebook contains detailed examples of all three scenarios above, as well as a short LangChain RAG demo to
+get a more realistic impression of how this feature will feel during your development loop.
+
+## Disabling and Re-enabling the Display
+
+This feature is enabled by default, but it can be turned off any time by calling `mlflow.tracing.disable_notebook_display()`.
+To remove the displays that have already rendered, you'll need to re-run the cells (or simply clear the cell output).
+
+If you want to re-enable the display, you can call `mlflow.tracing.enable_notebook_display()`.
+
+## Bug Reports and Feedback
+
+To report bugs or provide feedback, please file an issue in the
+[MLflow GitHub repo](https://github.com/mlflow/mlflow/issues). We're looking forward to hearing from you!

website/blog/authors.yml

@@ -115,3 +115,9 @@ jas-bali:
   title: Lead Specialist Solutions Architect at Databricks
   url: https://www.linkedin.com/in/jas-bali-195ba410a/
   image_url: /img/authors/jas_bali.png
+
+daniel-lok:
+  name: Daniel Lok
+  title: Software Engineer at Databricks
+  url: https://www.linkedin.com/in/daniel-yk-lok/
+  image_url: /img/authors/daniel_lok.png

website/src/posts.ts

@@ -26,6 +26,21 @@ export type Release = {
 
 // Sort by date descending
 export const BLOGS: Blog[] = [
+  {
+    title: "MLflow Tracing in Jupyter Notebooks",
+    path: "/blog/mlflow-tracing-in-jupyter",
+    tags: ["genai", "mlops", "tracing"],
+    authors: [
+      {
+        name: "Daniel Lok",
+        title: "Software Engineer at Databricks",
+        url: "https://www.linkedin.com/in/daniel-yk-lok/",
+        image_url: "/img/authors/daniel_lok.png",
+      },
+    ],
+    date: "2024-12-20",
+    thumbnail: "/img/blog/mlflow-tracing-in-jupyter.png",
+  },
   {
     title: "Using Bedrock Agent as an MLflow ChatModel with Tracing",
     path: "/blog/bedrock-chat-model-part-1",