The Ops Part

Continuous Integration Deployments of Adaptive Machine Learning Models

This talk focuses on the application of machine learning techniques in the detection of phishing attacks. The domain discussed involves deploying and calibrating machine learning models, as well as an automated workflow, to accurately detect phishing attempts. The objective is to provide a comprehensive overview of the current state-of-the-art techniques used in the field and how they can be effectively integrated into an automated system to minimize the risk of phishing attacks.

Gradual || YouTube

Let's Talk About Raw Documents // Crag Wolfe

MLOps community meetup #121! On February 22, we will be talking to Crag Wolfe, Infrastructure Team Lead at Unstructured.io.

Let's Talk About Raw Documents.
Modern ML pipelines still often need pre-processed documents. This isn't changing anytime soon, in fact, the appetite is growing. Unstructured.io is focused on extracting structured data from raw documents (pdf, pptx, html, etc).

Already excited? Check out Unstructured.io's open-source libraries!

Challenges of Feature Monitoring for Real-Time Machine Learning

To be successful with machine learning, you need to do more than just monitor your models at prediction time. You also need to monitor your features and prevent a “garbage in, garbage out” situation.

But it’s really difficult to detect problems with the data being served to your models—especially for real-time production ML applications like recommender systems or fraud detection systems.

In this blog post, Willem Pienaar, founder of Feast, dives into what feature monitoring for real-time ML entails and common data quality challenges, including:

Volatile dependencies on analytics teams
Computing and validating feature metrics
Limitations of current tools
Understanding and detecting data drift