How to Maintain Machine Learning Model Quality

Reliability, evaluation, and “hallucination anxiety” are where most AI programmes quietly stall. Not because the model is weak. Because the system around it is not built to scale trust. When companies move beyond demos, three hard questions appear: →Can we rely on this output? →Do we know what “good” actually looks like? →How much human oversight is enough? The fix is not better prompting. It is a strategy and operating discipline. 𝐅𝐢𝐫𝐬𝐭: ⁣Define reliability like a product, not a vibe. Every serious AI use case should have a one-page SLO sheet with measurable targets across: →Task success ↳Right-first-time rate and rubric-based acceptance →Factual grounding ↳Evidence coverage and unsupported-claim tracking →Safety and compliance ↳Policy violations and PII leakage →Operational quality ↳Latency, cost per task, escalation to humans Now “good” is no longer opinion. It is observable. 𝐒𝐞𝐜𝐨𝐧𝐝:  evaluation must be continuous, not a one-off demo test. Use a simple loop: 𝐏lan: Define rubrics, datasets, and risk tiers 𝐃⁣o: Run offline evaluations and limited pilots 𝐂heck: Monitor drift and regressions weekly 𝐀ct: Update prompts, data, guardrails, and workflows Support this with an AI test pyramid: →Unit checks for prompts and tool behaviour →Scenario tests for real edge failures →Regression benchmarks to prevent backsliding →Live monitoring in production Add statistical control charts, and you can detect silent degradation before users do. 𝐓𝐡𝐢𝐫𝐝: reduce hallucinations by design. →Run a short failure-mode workshop and engineer controls: →Require retrieval or evidence before answering →Allow safe abstention instead of confident guessing →Add claim checking and tool validation →Use structured intake and clarifying flows You are not asking the model to behave. You are designing a system that expects failure and contains it. 𝐅𝐨𝐮𝐫𝐭𝐡: make human-in-the-loop affordable. Tier risk: →Low risk: Light sampling →Medium risk: Triggered review →High risk: Mandatory approval Escalate only when signals demand it: low confidence, missing evidence, policy flags, or novelty spikes. Review becomes targeted, fast, and a source of improvement data. 𝐅𝐢𝐧𝐚𝐥𝐥𝐲: Operate it like a capability. Track outcomes, risk, delivery speed, and cost on a single dashboard. Hold a short weekly reliability stand-up focused on regressions, failure modes, and ownership. What you end up with is simple: ↳Use case catalogue with risk tiers ↳Clear SLOs and error budgets ↳Continuous evaluation harness ↳Built-in controls ↳Targeted human review ↳Reliability cadence AI does not scale on intelligence alone. It scales on measurable trust. ♻️ Share if you found thisuseful. ➕ Follow (Jyothish Nair) for reflections on AI, change, and human-centred AI #AI #AIReliability #TrustAtScale #OperationalExcellence

An AI model that "kind of" works isn’t good enough. Here’s 10 principle form the last IMDRF : 1) Define a clear intended use & involve experts Outline a precise intended use that meets clinical needs. Engage experts across disciplines to refine it and assess risks at every stage. 2) Strong engineering, design & security practices Ensure traceability, reproducibility, and data integrity. Apply robust security and risk management to protect patient safety. 3) Representative datasets for clinical evaluation Use datasets that reflect the real patient population. Diversity and sufficient size help ensure unbiased performance. 4) Independent training & test datasets Keep training and test datasets completely separate. Perform external validation based on risk levels. 5) Fit-for-purpose reference standards Use clinically relevant standards aligned with the intended use. If no standard exists, document the rationale for selection. 6) Model choice aligned with data & intended use Ensure model design fits the data and mitigates risks. Set clear performance goals and account for variability. 7) Human-AI interaction in device assessment Evaluate performance within clinical workflows. Consider human factors like skill level, autonomy, and misuse risks. 8) Clinically relevant performance testing Assess real-world performance independently from training data. Test across patient subgroups and factor in human-AI interactions. 9) Clear & essential user information Communicate intended use, limitations, and updates transparently. Ensure users understand model function, risks, and feedback mechanisms. 10) Ongoing monitoring & retraining risk management Continuously monitor models to ensure safety and performance. Use risk-based safeguards to manage bias, overfitting, and dataset drift. Developing AI/ML medical devices? These principles should be your foundation. Source: Good machine learning practice for medical device development: Guiding principles / IMDRF/AIML WG/N88 FINAL:2025

Every ML engineer eventually learns this the hard way: a model that shines on curated datasets can collapse the moment it meets reality. Why does this happen? 1️⃣ Dataset shift Your training data and production data rarely share the same distribution. - Covariate shift: Input features drift (e.g., new document layouts, changing user behavior). - Label shift: Class proportions evolve (e.g., new categories appear). - Concept drift: The underlying meaning of data changes over time. 2️⃣ Sampling bias "Sample data" is often too clean or too balanced, it fails to reflect the real-world frequency of messy, incomplete, or skewed inputs. 3️⃣ Overfitting to ideal conditions The model learns to exploit patterns that only exist in the sandbox, not in the wild. You see high validation accuracy but poor generalization. 4️⃣ Lack of robust evaluation If your test set looks like your training set, you're only measuring memorization, not adaptability. How to engineer robustness? - Data diversity > Data volume – collect from real production flows, not just preprocessed samples. - Simulate chaos – inject noise, missing values, OCR errors, edge cases, etc. - Cross-domain validation – test on out-of-distribution (OOD) samples early. - Continuous monitoring – track feature drift and model degradation post-deployment. - Retraining strategy – design pipelines for regular fine-tuning or active learning. - Simpler models often survive longer – complex architectures amplify sensitivity to data drift. Clean data gives you a demo. Dirty data gives you a product. #MachineLearning #AIEngineering #MLOps #DataQuality #Generalization #RoboustAI #LLM

Your AI model drops from 94% to 78% accuracy in six weeks. Same code. Same pipeline. What changed? The data feeding it changed. And nobody noticed until customers started complaining. This is data drift, and it kills more production models than bad architecture ever will. Model accuracy can degrade within days of deployment when production data diverges from training data. Most teams find out 3 to 6 weeks after it starts happening. Here's what actually works. Monitor input distributions continuously using statistical tests like Population Stability Index and Kolmogorov-Smirnov. These aren't complicated. PSI above 0.2 means your data has shifted enough to hurt predictions. Set up alerts when distributions change, not when accuracy drops. The timing matters. By the time your model performance metrics show problems, you've already lost weeks of good predictions. Distribution monitoring catches drift before it impacts users. Three patterns I see working in production at scale. First, track feature statistics daily and compare against training baselines. Second, set thresholds that trigger retraining automatically when drift exceeds acceptable levels. Third, keep recent production data ready so retraining happens in hours, not weeks. The economics are clear. Evidently AI reports that enterprises with drift detection avoid an average 3 to 6 week detection delay. That delay costs real money in wrong recommendations, missed fraud, and bad predictions that damage user trust. Your drift monitoring doesn't need to be fancy. It needs to run automatically, alert quickly, and connect directly to your retraining pipeline. Everything else is overhead. Most teams monitor model output. Almost nobody monitors the data feeding it. Until production breaks and executives ask why nobody saw it coming. #HumanWritten #ExpertiseFromForField #ProductionAI #MLOps #DataEngineering

You wouldn't cook a meal with rotten ingredients, right? Yet, businesses pump messy data into AI models daily— ..and wonder why their insights taste off. Without quality, even the most advanced systems churn unreliable insights. Let’s talk simple — how do we make sure our “ingredients” stay fresh? Start Smart → Know what matters: Identify your critical data (customer IDs, revenue, transactions) → Pick your battles: Monitor high-impact tables first, not everything at once Build the Guardrails: → Set clear rules: Is data arriving on time? Is anything missing? Are formats consistent? → Automate checks: Embed validations in your pipelines (Airflow, Prefect) to catch issues before they spread → Test in slices: Check daily or weekly chunks first—spot problems early, fix them fast Stay Alert (But Not Overwhelmed): → Tune your alarms: Too many false alerts = team burnout. Adjust thresholds to match real patterns → Build dashboards: Visual KPIs help everyone see what's healthy and what's breaking Fix It Right: → Dig into logs when things break—schema changes? Missing files? → Refresh everything downstream: Fix the source, then update dependent dashboards and reports → Validate your fix: Rerun checks, confirm KPIs improve before moving on Now, in the era of AI, data quality deserves even sharper focus. Models amplify what data feeds them — they can’t fix your bad ingredients. → Garbage in = hallucinations out. LLMs amplify bad data exponentially → Bias detection starts with clean, representative datasets → Automate quality checks using AI itself—anomaly detection, schema drift monitoring → Version your data like code: Track lineage, changes, and rollback when needed Here's the amazing step-by-step guide curated by DQOps - Piotr Czarnas to deep dive in the fundamentals of Data Quality. Clean data isn’t a process — it’s a discipline. 💬 What's your biggest data quality challenge right now?

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

90% of AI models degrade over time if not monitored/maintained. QB Labs developed Coda to automate model monitoring and enhance value post-deployment. Coda is supported by the LiveOps team at QuantumBlack, AI by McKinsey, which provides long-term monitoring support ensure sustained impact from deployed models and AI-driven solutions. Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI (Wiley, June 2023) describes AI/ML models as “living organisms” that change with underlying data. As such, it says, “they require constant monitoring, retraining and debiasing — a challenge with even a few ML models but simply overwhelming with hundreds of them.” Why is model maintenance a challenge? i) The world is constantly changing, and AI models are not isolated entities. Changes to the data they use, or to other integrated systems, have a knock-on effect. ii) New skills are needed. The skillsets required to support ongoing operations are different to those needed in the build phase of an AI project. iii) Tools for ongoing operations are different from those used to build models. The tools to sustain impact are not readily available off-the-shelf, which means projects often require custom implementations. Coda and the LiveOps team have 4 main focus areas: 1) A central dashboard to streamline workflows and offer visibility on all model maintenance operations, centralizing all actionable alerts 2) Troubleshooting, monitoring, and model retraining to sustain business impact and keep models relevant 3) Bridging capability gaps so teams can sustain optimal performance even if they lack some of the skillsets needed to do so independently 4) Diagnosis and intervention with automated, configurable analyses to build resolution plans, enabling rapid issue fixes + increased model stability. Great work by the product team driving this Andrew Ferris, Ben Horsburgh, Rohit Godha

Building the best AI model is only half the battle, it’s useless if it’s not usable… the real challenge is scaling it for production. Developing a cutting-edge model in the lab is exciting, but the true value of AI lies in deployment. Can your model handle the real-world pressures of scalability, latency, and reliability? 👉 How do you handle model drift when production data doesn’t match training data? Continuous monitoring with techniques like concept drift detection is crucial. 👉 Are you optimizing your inference time? Deploying large models efficiently requires leveraging techniques like quantization and model pruning to reduce size without sacrificing accuracy. 👉 Is your model robust to edge cases and unexpected inputs? Adversarial testing and uncertainty quantification ensure your AI performs reliably under a wide range of scenarios. Modeling isn’t just about accuracy, it’s about deployment, monitoring, and scaling. The difference between a good model and a great one is whether it delivers value consistently in production. What strategies are you using to ensure your models thrive in production? Let’s dig into the details👇 #AI #MachineLearning #ModelDeployment #Scalability #ModelDrift #ProductionAI #Optimization

A big concern for enterprise buyers right now is how AI companies maintain model accuracy, carry out model validation, and prevent hallucinations. I would like to quickly discuss how model validation applies to GenAI products, why it matters, and what we're doing at Parcha to provide highly accurate outputs to our customers. 🔍 Traditional Model Validation: In the world of traditional machine learning, model validation typically involves: 1. Training on historical data 2. Testing on a held-out dataset 3. Measuring performance with metrics like accuracy, precision, and recall 4. Periodic retraining as new data becomes available This approach works well for stable, predictable environments where the relationship between inputs and outputs remains relatively constant. 🤖 The GenAI Challenge: GenAI models, particularly large language models (LLMs), pose unique challenges: • They operate in open-ended domains with virtually infinite possible inputs • Their outputs can be highly context-dependent and creative • They can "hallucinate" or generate plausible-sounding but incorrect information • The concept of a "correct" answer is often subjective or situation-dependent Traditional validation methods need to be revised here. We can't test every potential input, and simple accuracy metrics don't capture these models' nuanced performance. 💡 Our Solution: At Parcha, we've developed the Parcha Model Validation Framework specifically for using LLMs in compliance. It's built on three key pillars: 1. Rigorous Validation: We go beyond simple accuracy metrics, using adversarial testing and domain-specific evaluations for the different types of tasks we solve for. 2. Continuous Monitoring: We track performance in real-time by quickly identifying any drift in model behavior and correcting it, which allows us to maintain over 98% accuracy 3. Proactive Improvement: We are developing a new prompt refinement system that optimizes prompts by using AI to improve them iteratively. 🚀 Why It Matters: Accuracy is essential for our customers who use Parcha to automate compliance workflows everyday. This framework ensures that our product continues to improve in accuracy beyond the initial pilot and rollout. 🤔 The Big Picture: As AI becomes more prevalent in business processes like compliance, robust validation frameworks tailored to LLMs will be critical. That's why we've invested a lot of engineering and data science resources into this area. We're also super grateful to Ankur Goyal at Braintrust, whom we've partnered with to evaluate Parcha's AI. Braintrust has been a super useful tool for helping us run, manage, and store evals and datasets. It is the foundation on which we built our prompt refinement system. In an upcoming blog post, we'll dive deeper into our model validation framework. Stay Tuned! How are you approaching model validation for your AI product? If you're a buyer, what are you looking for here?

𝐌𝐨𝐬𝐭 𝐀𝐈 𝐟𝐚𝐢𝐥𝐮𝐫𝐞𝐬 𝐚𝐫𝐞 𝐧𝐨𝐭 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥, 𝐓𝐡𝐞𝐲 𝐚𝐫𝐞 𝐀𝐈 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐅𝐚𝐢𝐥𝐮𝐫𝐞𝐬. Here are the 10 Principles that prevent costly Production Disasters: 𝟏.𝐏𝐑𝐎𝐌𝐏𝐓 𝐚𝐧𝐝 𝐌𝐎𝐃𝐄𝐋 𝐋𝐈𝐍𝐄𝐀𝐆𝐄 & 𝐕𝐄𝐑𝐒𝐈𝐎𝐍𝐈𝐍𝐆 • Version data, prompt code, and models (MLflow, DVC) • Track data sources and transformations • Support rollback and A/B testing 𝟐. 𝐂𝐋𝐄𝐀𝐑 𝐀𝐂𝐂𝐎𝐔𝐍𝐓𝐀𝐁𝐈𝐋𝐈𝐓𝐘 • Define RACI with escalation paths • Log decisions tied to model versions • Add approval gates at deploy and monitor stages 𝟑. 𝐑𝐄𝐀𝐋-𝐓𝐈𝐌𝐄 𝐎𝐁𝐒𝐄𝐑𝐕𝐀𝐁𝐈𝐋𝐈𝐓𝐘 • Monitor data, prediction, and concept drift • Set SLO alerts before performance drops • Maintain feedback loops with full lineage 𝟒. 𝐂𝐑𝐎𝐒𝐒-𝐅𝐔𝐍𝐂𝐓𝐈𝐎𝐍𝐀𝐋 𝐆𝐎𝐕𝐄𝐑𝐍𝐀𝐍𝐂𝐄 • Run regular AI risk and ethics reviews • Use NIST AI RMF for risk assessment • Gate high-risk models before launch 𝟓. 𝐅𝐀𝐈𝐑𝐍𝐄𝐒𝐒 & 𝐁𝐈𝐀𝐒 𝐓𝐄𝐒𝐓𝐈𝐍𝐆 • Audit fairness across protected groups • Monitor subgroup performance drift • Define acceptable parity metrics 𝟔. 𝐒𝐀𝐅𝐄 𝐅𝐀𝐈𝐋𝐔𝐑𝐄 𝐃𝐄𝐒𝐈𝐆𝐍 • Use circuit breakers and rule-based fallbacks • Enable fast rollout and rollback • Keep human workflows for edge cases 𝟕. 𝐒𝐋𝐎𝐬 𝐋𝐈𝐍𝐊𝐄𝐃 𝐓𝐎 𝐁𝐔𝐒𝐈𝐍𝐄𝐒𝐒 𝐊𝐏𝐈𝐬 • Define SLOs for latency, accuracy, and cost • Trigger alerts based on business impact • Automate fixes when limits are breached 𝟖. 𝐃𝐀𝐓𝐀 𝐆𝐎𝐕𝐄𝐑𝐍𝐀𝐍𝐂𝐄 & 𝐐𝐔𝐀𝐋𝐈𝐓𝐘 • Enforce data contracts and quality checks • Validate data before inference • Retrain when drift crosses limits 𝟗. 𝐄𝐗𝐏𝐋𝐀𝐈𝐍𝐀𝐁𝐈𝐋𝐈𝐓𝐘 & 𝐀𝐔𝐃𝐈𝐓𝐀𝐁𝐈𝐋𝐈𝐓𝐘 • Use SHAP/LIME for black-box models • Maintain model cards and datasheets • Log feature attributions for audits 𝟏𝟎. 𝐇𝐔𝐌𝐀𝐍-𝐈𝐍-𝐓𝐇𝐄-𝐋𝐎𝐎𝐏 • Provide ranked outputs with confidence • Capture human corrections • Define SLAs for manual review 𝐖𝐇𝐀𝐓 𝐓𝐄𝐀𝐌𝐒 𝐆𝐄𝐓 𝐖𝐑𝐎𝐍𝐆 They treat Governance as Paperwork, not Infrastructure. 𝐑𝐞𝐬𝐮𝐥𝐭: • Models deployed without lineage tracking • No Accountability when Failures occur • Drift goes undetected for Months • Bias discovered after impact • No rollback capability 𝐌𝐘 𝐑𝐄𝐂𝐎𝐌𝐌𝐄𝐍𝐃𝐀𝐓𝐈𝐎𝐍 Before production, verify: ✓ Lineage tracked? ✓ Accountability defined? ✓ Observability configured? ✓ Cross-functional review complete? ✓ Fairness tested? ✓ Failure modes designed? ✓ SLOs linked to KPIs? ✓ Data governance enforced? ✓ Explainability implemented? ✓ Human review defined? 𝐖𝐡𝐢𝐜𝐡 𝐩𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞 𝐚𝐫𝐞 𝐲𝐨𝐮 𝐦𝐢𝐬𝐬𝐢𝐧𝐠? ♻️ Repost this to help your network get started ➕ Follow Anurag(Anu) Karuparti for more PS: If you found this valuable, join my weekly newsletter where I document the real-world journey of AI transformation. ✉️ Free subscription: https://lnkd.in/exc4upeq #GenAI #AIAgents #AIGovernance