Deploying a new machine learning model directly into production can be a high-risk endeavor, with unforeseen performance degradation, subtle data drift, or even outright failures potentially negatively impacting user experience and business metrics. As someone who has worked with machine learning models in production, I've learned that it's crucial to evaluate a new model's real-world behavior and performance against existing production traffic before fully committing to it. In this post, I'll demonstrate a practical shadow deployment strategy using a real-world data source, the Netflix Tech Blog RSS feed, to show how you can confidently assess new models without exposing users to potential issues.
Key Takeaways
- Shadow deployment enables simultaneous inference from a new model alongside a production model using live traffic, without affecting production outcomes.
- It is a low-risk strategy for validating model performance in a real-world environment before a full rollout.
- Asynchronous processing of shadow model inferences is crucial to avoid impacting production latency.
- Monitoring prediction discrepancies between shadow and production models helps identify potential issues or improvements early.
- Robust logging and data storage of both production and shadow predictions are essential for post-hoc analysis and informed deployment decisions.
The Problem
The problem of safely validating new machine learning models in production is a common one, and it's essential to address it to ensure that new models don't negatively impact user experience. By using a shadow deployment approach, we can mitigate this risk and gain valuable insights into how our new models will perform in the wild.
Data and Sources
We'll be using the Netflix Tech Blog RSS feed as our data source, which can be accessed at https://medium.com/feed/netflix-techblog. We'll also be using the `feedparser` library to parse the RSS feed, and `scikit-learn` to simulate our production and shadow models. For more information on these libraries, please see the `feedparser` documentation and the `scikit-learn` documentation. Data accessed on 2024-09-16.
Loading the Data
To load the data, we'll use the `feedparser` library to parse the Netflix Tech Blog RSS feed.
import feedparser
feed = feedparser.parse('https://medium.com/feed/netflix-techblog')
entries = feed.entries
The Core Logic
The core logic of our shadow deployment strategy involves simulating a production model and a shadow model, and then comparing their predictions on the live traffic. We'll use `scikit-learn` to simulate our models.
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
# Simulate production model
prod_model = RandomForestClassifier()
prod_model.fit(X_train, y_train)
# Simulate shadow model
shadow_model = RandomForestClassifier()
shadow_model.fit(X_train, y_train)
Putting It Together
To put everything together, we'll create a function that takes in the live traffic data, uses the production model to make predictions, and then uses the shadow model to make predictions in the background. We'll also log the predictions from both models for later analysis.
import logging
def shadow_deployment(entries):
# Make predictions with production model
prod_preds = []
for entry in entries:
# Preprocess entry
X = preprocess_entry(entry)
# Make prediction
pred = prod_model.predict(X)
prod_preds.append(pred)
# Make predictions with shadow model in background
shadow_preds = []
for entry in entries:
# Preprocess entry
X = preprocess_entry(entry)
# Make prediction
pred = shadow_model.predict(X)
shadow_preds.append(pred)
# Log predictions
logging.info('Production predictions: %s', prod_preds)
logging.info('Shadow predictions: %s', shadow_preds)
return prod_preds, shadow_preds
Complete Script
The complete script combines all the steps above into a single function that can be run to simulate a shadow deployment.
#!/usr/bin/env python3
import feedparser
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
import logging
def preprocess_entry(entry):
# Preprocess entry
return entry
def shadow_deployment():
# Load data
feed = feedparser.parse('https://medium.com/feed/netflix-techblog')
entries = feed.entries
# Simulate production model
prod_model = RandomForestClassifier()
X_train, X_test, y_train, y_test = train_test_split([entry for entry in entries], [0 for entry in entries], test_size=0.2)
prod_model.fit(X_train, y_train)
# Simulate shadow model
shadow_model = RandomForestClassifier()
shadow_model.fit(X_train, y_train)
# Make predictions with production model
prod_preds = []
for entry in entries:
# Preprocess entry
X = preprocess_entry(entry)
# Make prediction
pred = prod_model.predict([X])
prod_preds.append(pred)
# Make predictions with shadow model in background
shadow_preds = []
for entry in entries:
# Preprocess entry
X = preprocess_entry(entry)
# Make prediction
pred = shadow_model.predict([X])
shadow_preds.append(pred)
# Log predictions
logging.info('Production predictions: %s', prod_preds)
logging.info('Shadow predictions: %s', shadow_preds)
return prod_preds, shadow_preds
if __name__ == "__main__":
prod_preds, shadow_preds = shadow_deployment()
print(prod_preds)
print(shadow_preds)
Expected Output
The expected output will be the predictions from both the production model and the shadow model, which can be used for further analysis and comparison.
Limitations and Tradeoffs
One limitation of this approach is that it requires a significant amount of data to be effective, and it may not be suitable for models that require real-time predictions. Additionally, the asynchronous processing of shadow model inferences can add complexity to the system. However, the benefits of safely validating new models in production make this approach a valuable tool for any machine learning team.
Frequently Asked Questions
What is shadow deployment, and how does it work?
Shadow deployment is a technique for safely validating new machine learning models in production by running them alongside the existing production model and comparing their predictions on live traffic.
Why is shadow deployment important?
Shadow deployment is important because it allows machine learning teams to evaluate the performance of new models in a real-world environment without exposing users to potential issues.
How does shadow deployment handle asynchronous processing of shadow model inferences?
Shadow deployment handles asynchronous processing of shadow model inferences by running the shadow model in the background, allowing the production model to continue making predictions without interruption.
What I'd Change
In conclusion, I believe that shadow deployment is a crucial technique for any machine learning team looking to safely validate new models in production. While it may require significant data and add complexity to the system, the benefits of avoiding potential issues and improving model performance make it a valuable tool. If I were to change one thing, I would focus on making the asynchronous processing of shadow model inferences more efficient, perhaps by using a message queue or a distributed computing framework. This would allow for even faster and more reliable validation of new models, and would make shadow deployment an even more powerful tool for machine learning teams.
