How to Maintain Machine Learning Model Quality

Scaling MLOps on AWS: Embracing Multi-Account Mastery 🚀 Move beyond the small team playground and build robust MLOps for your growing AI ambitions. This architecture unlocks scalability, efficiency, and rock-solid quality control – all while embracing the power of multi-account setups. Ditch the bottlenecks, embrace agility: 🔗 Multi-account mastery: Separate development, staging, and production environments for enhanced control and security. 🔄 Automated model lifecycle: Seamless workflow from code versioning to production deployment, powered by SageMaker notebooks, Step Functions, and Model Registry. 🌟 Quality at every step: Deploy to staging first, rigorously test, and seamlessly transition to production, all guided by a multi-account strategy. 📊 Continuous monitoring and feedback: Capture inference data, compare against baselines, and trigger automated re-training if a significant drift is detected. Here's how it unfolds: 1️⃣ Development Sandbox: Data scientists experiment in dedicated accounts, leveraging familiar tools like SageMaker notebooks and Git-based version control. 2️⃣ Automated Retraining Pipeline: Step Functions orchestrate model training, verification, and artifact storage in S3, while the Model Registry keeps track of versions and facilitates approvals. 3️⃣ Multi-Account Deployment: Staging and production environments provide controlled testing grounds before unleashing your model on the world. SageMaker endpoints and Auto Scaling groups handle inference requests, powered by Lambda and API Gateway across different accounts. 4️⃣ Continuous Quality Control: Capture inference data from both staging and production environments in S3 buckets. Replicate it to the development account for analysis. 5️⃣ Baseline Comparison and Drift Detection: Use SageMaker Model Monitor to compare real-world data with established baselines, identifying potential model or data shifts. 6️⃣ Automated Remediation: Trigger re-training pipelines based on significant drift alerts, ensuring continuous improvement and top-notch model performance. This is just the tip of the iceberg! Follow Shadab Hussain for deeper dives into each element of this robust MLOps architecture, explore advanced tools and practices, and empower your medium and large teams to conquer the AI frontier. 🚀 #MLOps #AI #Scalability #MultiAccount #QualityControl #ShadabHussain

Machine learning models aren’t a “build once and done” solution—they require ongoing management and quality improvements to thrive within a larger system. In this tech blog, Uber's engineering team shares how they developed a framework to address the challenges of maintaining and improving machine learning systems. The business need centers on the fact that Uber has numerous machine learning use cases. While teams typically focus on performance metrics like AUC or RMSE, other crucial factors—such as the timeliness of training data, model reproducibility, and automated retraining—are often overlooked. To address these challenges at scale, developing a comprehensive platform approach is essential. Uber's solution involves the development of the Model Excellence Scores framework, designed to measure, monitor, and enforce quality at every stage of the ML lifecycle. This framework is built around three core concepts derived from Service Level Objectives (SLOs): indicators, objectives, and agreements. Indicators are quantitative measures that reflect specific aspects of an ML system’s quality. Objectives define target ranges for these indicators, while Agreements consolidate the indicators at the ML use-case level, determining the overall PASS/FAIL status based on indicator results. The framework integrates with other ML systems at Uber to provide insights, enable actions, and ensure accountability for the success of machine learning models. It’s one thing to achieve a one-time success with machine learning; sustaining that success, however, is a far greater challenge. This tech blog provides an excellent reference for anyone building scalable and reliable ML platforms. Enjoy the read! #machinelearning #datascience #monitoring #health #quality #SLO #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/g6DJm9pb

Machine learning is different than anything else. For the most part, you build software once and run it forever. (In 2012, I ran a game I had built in 1997. Everything ran as it had on the first day.) But, a machine learning model is outdated the minute you release it. Building a model is easy. The challenge is keeping that model alive. The world constantly changes and drifts away from the data we use to train a model. Unfortunately, this is not as obvious as it sounds. Many companies have no idea they need to deal with this problem. One way we deal with change is monitoring: • Monitoring the data coming into the model • Monitoring the hardware operational metrics • Monitoring the model predictions • Monitoring the feedback from model users Monitoring helps maintain model reliability over time. Unfortunately, good monitoring tools are not as common as you might think. I work with the team @Cometml, and their model monitoring tool is one of the best I've used: https://lnkd.in/eurTrWpS And SageMaker's monitoring is one of the worst. Easiest way to join the XXI century is by using a tool to: 1. Identify if your model is struggling 2. Track drift across inputs and outputs 3. Get alerts if anything is wrong Not having a robust monitoring system is like driving a car with a blacked-out windshield.