Applying Continuous Machine Learning in Industry

I really liked this paper because it shows a practical way to make agents learn on the job without touching model weights. Memento stores past task traces as cases, then learns a small neural policy to pull the right ones when planning with tools. The result is strong and cheap adaptation in the wild, with top performance on GAIA validation and solid gains on DeepResearcher and SimpleQA. The most useful detail for builders is that a small, well curated memory works best, not a giant one. For Agriculture and Industry Applications: 1) Continuous learning without retraining: Treat every advisory or workflow as a case with state, action, and outcome. Let the agent improve day by day from farmer interactions, sensor reads, and post‑season outcomes, without fine‑tuning base models. 2) Planner‑executor pattern with tools: Use the planner to decompose tasks and the executor to call tools. In ag these tools include weather APIs, satellite and drone imagery, soil and pest models, market prices, compliance rulebooks, PDFs from ag departments, and ERP data. 3) Case Bank as an agronomy brain: Store both wins and misses. When a new query arrives, retrieve a few similar cases and adapt. Start non‑parametric for speed, add a lightweight Q‑scorer to rank cases once feedback accumulates. 4) Keep K small: Retrieve about four cases per query to avoid noise and token bloat. Curate continuously and prune low‑utility items so retrieval stays sharp. 5) Define rewards that matter: Use field‑level outcomes and ops metrics as feedback signals: yield delta, pest suppression, irrigation savings, task completion time, compliance pass, and advisor override rate. 6) Instrumentation and audit: Log plan steps, tool calls, and retrieved cases so agronomists can review decisions and trust the recommendations. 7) Cost control: Most cost comes from growing input context on harder tasks, not from long answers. Summarize tool outputs, chunk logs, and snapshot intermediate state to cap tokens. 8) Generalize to new regions and crops: Case‑based memory helps on out‑of‑distribution tasks, which maps well to new geographies, varieties, and policy changes.

Working with companies on their AI strategy across different industries, I get to see lots of common patterns… Data quality and availability: AI needs consistent, reliable inputs. In healthcare, for example, patient records often live in multiple EHR systems that don’t speak to each other—lab results here, imaging scans there, medication histories somewhere else. A predictive model can’t learn if half its data is missing or mislabeled. Similarly, in manufacturing, sensor data may come from old PLCs with proprietary formats, making it nearly impossible to combine vibration readings, temperature logs, and maintenance histories into one dataset. Without a unified data platform and a clear governance process, teams spend weeks just cleaning and standardizing fields—time they could have spent building models that actually work. Talent gap: Skilled AI professionals are hard to hire. When you can’t find enough in-house experts, decide: build or buy? Many organizations skip the learning curve by licensing vertical AI solutions—fraud detection engines for finance, demand-forecasting tools for retail—rather than trying to do it all themselves. Legacy systems: Modern AI tools often can’t plug into decade-old infrastructure. Whether it’s a mainframe transaction log or a factory’s outdated control system, integration work can eat up months. By the time the AI finally connects, its assumptions are already stale. Ethical concerns and bias: If your training data reflects past imbalances—biased loan approvals or skewed hiring pools—AI can repeat those mistakes at scale. In any sector where decisions affect real lives, unchecked bias means unfair outcomes, reputational damage, and possible legal headaches. Privacy and security: AI often needs sensitive information—patient scans, credit-card histories, or purchase patterns—to work. If that data isn’t anonymized and encrypted, a single breach can lead to fines, lawsuits, and lost trust. Industries under strict regulations must lock down access and track every data touchpoint. Scalability: A model that works in a small prototype often crashes under real-world demand. Recommendations that run smoothly for a few hundred users can grind to a halt during peak traffic. Route-optimization logic that handles a handful of delivery trucks can buckle when dozens more come online. Without cloud infrastructure that auto-scales, automated retraining, and clear MLOps pipelines, pilots stay pilots—and never deliver real impact. Ignore these six, and your AI spend becomes a lesson in frustration. Nail them—clean data, the right build-vs-buy decision, seamless integration, bias checks, airtight security, and a plan for scale—and you’ll turn AI from a buzzword into a repeatable growth engine.

🔎 Many industrial operators face the same challenge: "How can we use AI to detect anomalies early enough to prevent unplanned downtime?" That’s a question I often hear in conversations with customers. During a recent visit with Daniel Mantler, our product manager for edge computing, he shared a use case that addresses exactly this challenge. As we all know by now, AI is no longer rocket science. But getting it into real life industrial applications still seeems to be. And that's where our team of experts developed a lean and fast to adapt setup that uses local sensor data to detect for example vibration, temperature, or anomalies directly at the machine. A lightweight machine learning model runs on an edge device and identifies deviations from normal behavior in real time. Because the data is processed on-site, latency is minimal and data sovereignty is maintained. Both aspects are critical in many industrial environments. But the real value lies in the practical benefits for operators: Faster reaction times, reduced dependency on external infrastructure, and the ability to integrate AI into existing systems without needing a team of data scientists. What are your thoughts on integrating ML into edge architectures? I’m keen to hear your thoughts. Let’s use the comments to share perspectives and learn from one another. For those who want to dive deeper into the technical setup and learnings, here’s the full article: 🔗 https://lnkd.in/e8Z5HMCH #artificialintelligence #machinelearning #edgecomputing

Modern manufacturing excellence requires seamless integration of machine learning operations (MLOps) within converged IT/OT environments, creating the foundation for true Industrial DataOps. This structured approach enables organizations to deploy, monitor, and continuously improve AI models while maintaining data integrity. Three 🔑 core capabilities manufacturers must have: 1️⃣ Continuous Model Evolution: MLOps pipelines automatically retrain models as production conditions change, maintaining detection accuracy and preventing model drift that would otherwise lead to increased false positives or missed quality issues. 2️⃣ Cross-Disciplinary Collaboration: Standardized governance frameworks like Unity Catalog create common ground where data scientists, IT specialists, and manufacturing engineers can jointly develop, test, and deploy AI solutions that respect operational constraints while leveraging enterprise data resources. 3️⃣ Scalable System Architecture: A properly implemented MLOps strategy enables organizations to scale successful AI implementations from pilot projects to enterprise-wide deployments, replicating proven models across multiple facilities while preserving crucial site-specific customizations. #IndustrialAI #AI #Governance

Are you in the manufacturing industry? Your products have to be tested to fulfill SLA. I think it's a good idea to incorporate AI into your operations. Embracing technology is not just a trend; it's a strategic evolution that can optimize your processes and enhance your returns on investment. Here are three ways AI can assist you in quality control within manufacturing: 💡 Use Case: Visual Inspection with Computer Vision AI-powered cameras and computer vision models are used to detect defects in products on the production line—such as cracks, misalignments, or surface anomalies. Example: A car parts manufacturer deployed AI vision systems to inspect brake pads. Traditional inspection missed micro-cracks that led to safety recalls. The AI model was trained on thousands of defect images and deployed on the line, instantly flagging faulty items. Impact: 🥊 Reduced defect rates by 40% 🏎️ Increased inspection speed by 3× 🗃️ Improved regulatory compliance Use Case: Predictive Maintenance for Equipment Quality Machine learning models predict when a manufacturing machine is likely to fail or degrade, which helps maintain product consistency and prevents defect-prone operation. Example: A steel rolling plant used sensor data (vibration, temperature, acoustics) to predict mill misalignments that were causing warped sheets. AI alerted technicians hours before quality dropped. Impact: 🗑️ 25% decrease in production waste 💪 30% increase in uptime 🔎 Improved consistency across batches Use Case: AI-Driven Root Cause Analysis AI analyzes production data across various stages to identify the root cause of recurring quality issues that human teams struggle to pinpoint. Example: An electronics assembly line faced sporadic soldering defects. An AI system correlated the defects with temperature shifts in a nearby process that wasn't being monitored as a quality variable. Impact: 💊 Reduced quality incidents by 50% ⏳ Accelerated RCA from days to hours 🛠️ Enabled proactive process adjustments Harnessing AI to tailor solutions to your specific needs can revolutionize your manufacturing processes. #AI #Manufacturing #QualityControl