Learn Vertex AI while building a fraud detection system
TLDRIn the Google IO 2022 presentation, Ivan Nardini introduces the concept of MLOps and how it can be applied to build a modern, data-driven fraud detection system on Google Cloud's Vertex AI platform. The story begins with a banking company's data science team, tasked with creating an innovative solution to replace a rules-driven engine with a machine learning model that can estimate fraud probabilities without manual intervention and is interpretable for validation and improvement. Nardini discusses the challenges of the old system, including bias, scalability, and maintenance issues, and then guides the audience through the process of building a new system using Vertex AI. This includes data ingestion, feature creation with BigQuery, real-time feature calculation challenges, and the importance of a Feature Store for preventing data leakage and ensuring model consistency. The presentation concludes with a demonstration of the Fraud Finder application, showcasing how Vertex AI can be used to streamline the ML lifecycle, from training to deployment and monitoring, emphasizing the importance of collaboration and learning within a team.
Takeaways
- 🚀 The story begins with joining a data science team at a major banking company, responsible for building innovative solutions across business units.
- 🎯 The current fraud detection system is rules-driven with inherent biases, difficult to scale, and hard to maintain due to the dynamic nature of fraud patterns.
- 🌟 The vision is to build a modern, data-driven fraud detection engine using machine learning on Google Cloud to estimate the probability of fraud without manual intervention and with interpretability.
- 🛠️ The importance of considering the entire system's requirements and dependencies when selecting machine learning models and associated technology.
- 🔍 High-quality data is crucial for generating insightful models; without it, the model's performance will suffer.
- 🧱 MLOps is the integration of machine learning development with operations to put models into production, emphasizing culture, practice, and technology.
- 🌐 Google Cloud's Vertex AI is a managed machine learning platform designed to accelerate the experimentation and deployment of ML models at scale.
- 🔧 The building blocks of Vertex AI cover the entire lifecycle from data ingestion to model training, prediction, and monitoring for production analysis.
- 🏗️ Building a new fraud detection system on Vertex AI involves starting with historical transaction data, transforming it into features relevant for fraud prediction.
- ⏱️ Introducing a Feature Store is essential for addressing data leakage by providing point-in-time lookups and serving features at scale with low latency.
- 🤖 The Fraud Finder project is an example of applying data science and machine learning to detect fraudulent transactions at scale in real time, leveraging Vertex AI's capabilities.
Q & A
What was the main issue with the existing rules-driven engine for fraud detection described in the script?
-The main issue with the existing rules-driven engine was that it was biased, hard to scale, and difficult to maintain due to the subjective nature of the rules, the requirement for hard-coding new rules for each discovered fraud pattern, and the high turnover of investigators and software engineers involved in the rule-making process.
What are the three key challenges Maya identified with the current fraud detection system?
-The three key challenges identified by Maya were the inherent bias in human rules, difficulty in scaling the system due to the need for hard-coding rules, and the challenge of containing and maintaining the system as it grew with an increasing number of rules for different fraud scenarios.
What was the main requirement Maya had for the new fraud detection system?
-Maya required the new system to be a modern, data-driven fraud detection engine that uses machine learning to estimate the probability of each transaction being a fraud, without any manual intervention, and yet be interpretable for validation and potential improvement by SMEs and investigators.
What is MLOps and how does it relate to the development of machine learning models?
-MLOps is the set of cultural practices and technologies that aim to unify machine learning development with operations to deploy models into production. It emphasizes the importance of not just building machine learning models, but also ensuring they are fed with high-quality data, modular to adapt to new patterns, and part of a larger system that considers the context for production analysis and generating business value.
How does Vertex AI help in addressing the challenges of building a fraud detection system?
-Vertex AI is a Google Cloud managed machine learning platform that helps accelerate the experimentation and deployment of ML models at scale. It provides building blocks to cover the entire lifecycle, from data ingestion to model training, prediction, and monitoring, enabling production analysis of models and addressing the challenges of scalability, maintainability, and interpretability.
A Feature Store provides a service to address data leakage by offering point-in-time lookups to fetch the most up-to-date features with respect to the time labels become available. It serves features at scale with low latency, ensuring alignment of features with labels and mitigating training and serving skew.
-null
How does the fraud detection system ensure it uses the most representative features for training and prediction?
-The system ensures the use of the most representative features by employing a Feature Store, which provides the latest features aligned with the labels before they become available. This helps maintain consistency and relevance of the features used for both training and online predictions.
What was the outcome of using Vertex AI for the fraud detection system project named Fraud Finder?
-Fraud Finder, built using Vertex AI, successfully implemented a data application that could stream transactions, classify them as fraudulent or non-fraudulent with respect to a probability threshold, and provide insights such as latency profile and distribution of fraudulent and non-fraudulent transactions, demonstrating the effectiveness of Vertex AI for MLOps.
How does the real-time aspect of the fraud detection system affect the calculation and use of features?
-In a real-time system, some features cannot be calculated on-the-fly when serving the model. The system must use a data store optimized for low-latency lookup operations at scale to provide the most up-to-date features instantly, ensuring that the model can generate online predictions effectively.
What does Ivan Nardini emphasize as the most crucial element for success in machine learning and MLOps?
-Ivan Nardini emphasizes that while technology, culture, and practices are important, the most crucial element for success in machine learning and MLOps is people – their collaboration, mutual learning, and teamwork across different skills and backgrounds.
Outlines
🚀 Introduction to Modernizing Fraud Detection with Google Cloud
Ivan Nardini, a Customer Engineer at Google Cloud, introduces a scenario involving a new data science team at a major banking company tasked with creating innovative solutions. On their first day, they meet Maya, the product manager for the Fraud Detection System, who presents the current rules-driven, hard-to-scale system plagued with biases and maintenance issues. She expresses a desire to replace it with a modern, data-driven engine built on Google Cloud, utilizing machine learning to estimate fraud probabilities without manual intervention and maintaining interpretability for SMEs and investigators. Ivan emphasizes the importance of MLOps, a practice combining ML development and operations to streamline the production of ML models, and introduces Vertex AI, Google Cloud's ML platform, to address these needs.
🛠 Building a Real-Time Fraud Detection System on Vertex AI
The narrative transitions into the technical details of constructing a fraud detection system named Fraud Finder on Vertex AI, inspired by real customer needs. It begins with data preparation, highlighting the challenges of real-time feature calculation and alignment with transaction labels. To address these, Ivan discusses the introduction of a Feature Store for up-to-date, low-latency feature access. He outlines the steps from data ingestion and model training to deployment, emphasizing the significance of MLOps practices for scaling and managing the machine learning lifecycle efficiently. Ivan also touches upon the challenges of training with continuously updated data and the solution provided by Feature Store to align features with labels accurately.
🌟 Showcasing Fraud Finder: A Vertex AI Success Story
Ivan showcases Fraud Finder, a project demonstrating the application of Vertex AI for fraud detection. The system starts with streaming transactions, applies machine learning to identify fraudulent activities, and displays predictions in real-time. The Dashboard view offers insights into the system's latency and the distribution of fraudulent versus non-fraudulent transactions. Ivan concludes by highlighting the transformation from a biased, hard-to-scale, and maintenance-intensive system to an efficient, data-driven model facilitated by Vertex AI. He stresses the importance of teamwork, diverse skills, and backgrounds in achieving success in machine learning and MLOps, inviting the audience to start their journey in implementing ML models on Google Cloud.
Mindmap
Keywords
💡Fraud Detection System
💡Data Science Team
💡Machine Learning Model
💡Google Cloud
💡MLOps
💡Vertex AI
💡Feature Store
💡Data Transformation
💡Real-Time Fraud Detection
💡Model Training
💡Model Serving
Highlights
Introduction to building a fraud detection system on Google Cloud by Ivan Nardini at Google IO 2022.
Challenges with the existing rules-driven engine include bias, difficulty in scaling, and maintenance issues.
The need for a modern, data-driven fraud detection engine that is automated and interpretable.
The importance of considering the entire system's requirements and dependencies when selecting machine learning models and technology.
The concept of MLOps as a combination of culture, practice, and technology to unify ML development with operations.
Google's expertise in MLOps with thousands of ML models training concurrently and deploying globally.
Introduction to Vertex AI, Google Cloud's managed machine learning platform for accelerating ML model experimentation and deployment.
Fraud Finder, a project by Google Cloud to apply state-of-the-art data science and machine learning for detecting fraudulent transactions at scale.
Starting with historical transaction data and the necessity of data transformation to derive relevant features for fraud prediction.
The use of BigQuery for analyzing large datasets and the challenges of real-time fraud detection systems with high transaction volumes.
The concept of a Feature Store to address data leakage and provide point-in-time lookups for the most up-to-date features aligned with labels.
Training the model offline with features aligned with labels and using a notebook environment for experiments and model evaluation.
Formalizing model training as a pipeline component for reproducing model training at scale with machine learning pipelines.
Deploying models to the serving environment only after meeting certain performance threshold levels.
Fraud Finder's data application showcasing real-time transaction classification, fraud detection, and latency profile.
The significance of teamwork and collaboration in overcoming challenges and achieving success in machine learning and MLOps.