Everyone. I'm Lolita Lanka, currently working as a senior, a PN developer at US Food and Drug Administration. Today I'll be talking about scaling AI in education through ML ops. Unlike enterprises, schools face unique challenges such as limited budgets, diverse, revised, and educators who aren't Mr. Engineers. And that's why we need a different approach, like low-code platforms, privacy first design, and offline ready architectures that make AI practical and inclusive for classrooms. Over the next few minutes, I will walk you through the challenges, strategies, and results we have seen in bringing ML ops to education. Welcome to today's session on machine learning operation strategies tailored for educational environments. As AI and ML continue to evolve, it is crucial for schools and educational institutions to embrace these technologies despite resource constraints. In this presentation, we will explore strategies that empower educational stakeholders to effectively integrate AI tools into their institutions. We'll begin by examining the educational ML ops landscape where we will discuss the unique challenges faced by schools such as limited resources and the current gaps in technology infrastructure. Next, we will explore low-code platforms as enablers, showcasing how these platforms can democratize AI deployment, allowing non-technical users to take part in the process. This is an essential part of ensuring that AI tools are accessible to everyone. Not just those with deep technical expertise. Well then let dive into ML ops Architecture for Education, where we will discuss the balance between rapid AI development and the need for robust governance, security, and compliance, which are often crucial in educational settings. We'll also highlight implementation strategies, focusing on practical approaches like containerization and federated learning, which can help schools implement AI solutions effectively, even with limited resources. Finally, we will provide key takeaways and future directions outlining actionable strategies that can directly apply to your educational settings, helping you navigate the complexities of AI deployment and setting the stage for future innovations. As educational institutions begin to integrate artificial intelligence and machine learning technologies into their operations and curricula, they face a unique set of challenges that are distinct from those encountered in other sections. These challenges are influenced by various factors such as budget constraints, infrastructure disparities, and the need for specialized expertise. Unlike enterprise level AI implementations, schools and universities must account for additional considerations like governance, privacy, and the diverse technological environments in in which these systems will be deployed. This slide outlines the key obstacles educational institutions encounter when adopting AI system and ML Ops practices. Understanding these challenges is the first step toward identifying solutions that can make AI deployment accessible, affect you, and sustainable in schools and educational environments. Let's discuss the challenges one by one with some examples. Infrastructure disparity. The infrastructure available in educational institutions varies widely, especially between urban and rural settings. In urban environments, schools may have access to high speed internet and advanced computational resources. However, schools in rural or underprivileged areas may struggle with limited internet connectivity and outdated hardware. Example in well connected cities, a school can deploy machine learning tools and cloud-based AI platforms seamlessly. However, a rural school in a remote location may face difficult even accessing internet making cloud-based AI solutions challenging. Additionally, high performance computing infrastructures required for intensive machine learning tasks might not be bio available, which would limit the AI system's effectiveness. The second is the technical expertise gaps. Most educators are not trained in specialized fields such as DevOps or machine learning engineering. As a result, they may lack the necessary technical skills to implement, maintain, or even troubleshoot AI solutions effectively. This skills gap makes it difficult to leverage AI systems without external support or significant training. Example, a school that introduces an AI powered educational tool to assist with grading and personalized learning may find it difficult to handle technical issues such as training new machine learning models, integrating the system with existing platforms, or maintaining it over time. For instance, an AI tool that helps with automating lessons, plans or grading may require constant updates and maintenance, which requires technical expertise that may educators do not have. Third is strict governance and privacy. Educational institutions must adhere to strict governance frameworks, especially when handling sensitive student data. This includes ensuring compliance with privacy laws such as Family educational Rights and Privacy Act called third part in the United States, which protects the privacy of student records. A systems in schools must comply with these privacy standards to ensure that they do not violate student rights. Example, when planning an AI power platform that analyzes student performances, it's crucial to ensure that no unauthorized access to student record occurs. And the data is used appropriately. Any AI tool that process students' data must have stringent access controls and security measures in place to provide reaches or misuse. For example, a school should could use a machine learning model to predict student success, but the model must ensure the data, like grades, behavior, records, and other personal details remain confidential and are securely stored. Next is the resource constraints. Many educational institutions operate on limited budgets, which can restrict their ability to invest in dedicated infrastructure or specialized personal for AI de deployments. This makes it challenging to purchase the high end server software or professional development needed to implement AI solutions effectively. Example, a small school district might struggle to fund cloud-based machine learning services. As a result, they may have to rely on low cost or open source platforms, which might not have the same functionality or support as commercial systems for instead, instruc using a commercial AI based tool for personalized learning, a school may have to use a simpler, free alternative that lacks robust features or scalability. Finally, heterogeneous device ecosystems. In many educational settings, there is a mix of different devices, laptops, desktops, tablets, and smartphones that students and teachers use. These devices often have different operating systems and performance capabilities, making it difficult to standardize AI system deployments across the diverse device ecosystem. Example, a machine learning platform designed for students may not be compatible with all devices in the school. For example, a high-end AI-based educational application might run well on modern laptops, but perform poorly or not at all on older tablets or low spec computers. Consider a scenario where a district deploys an AI tool to monitor student progress in real time. The tool might work on newer Windows laptops, but failed to perform as expected on outdated Chromebooks or tablets, limiting its effectiveness across the entire student body. These unique challenges faced by educational institutions require a tailored approach to ML ops, which is significantly different from the typical enterprise implementations used in larger corporations. Solutions that work in business often cannot be directly applied to schools without considering the specific needs and constraints of the education sector. Education focused ML ops strategies should consider factors like limited technical expertise, tight budgets, diverse devices, and the importance of strict privacy and governance. For example, local platforms or hybrid cloud solutions can offer these more accessible, cost effective parts for schools to integrate ai. By understanding these challenges and adjusting strategies accordingly, schools can still unlock the potential of AI while navigating these barriers effectively. Having discussed the broader challenges in deploying AI systems in educational institutions such as infrastructure disparities and limited expertise, it is important to understand that the nature of data in these settings presents its own unique complexities. Educational data does not behave like data from techn typical business environments. Instead, it exhibits distinct patterns and constraints that impact how machine learning models are trained, monitored, and maintained. In this slide, we will divide into the data distribution challenges specific to educational setting and how they require specialized approaches for effective AI implementation. Seasonal behavior patterns. Student interactions with educational patterns. Platforms follow the academic calendar, meaning usage spikes and drops aligned. Its semesters, holidays, and exam periods, for example, during summer breaks or holiday seasons, platform activity significantly decreases without accounting for this. AI models might mistake these predictable changes for a problem with model accuracy, triggering false alerts of model drift when the shift is actually natural and expected. Age-based distribution shifts as students progress through different grade levels. Their learning behaviors and interaction patterns evolve. For instance, a sixth graders engagement style with digital content is quite different from that of a 12th graders preparing for college entrance exams. AI system needs to distinguish these natural ships from genuine performance degradation. Otherwise, a model trained on younger students data might underperform or flag errors when applied to older students leading to unnecessary training or inaccurate predictions. Limited data volume. Many smaller or rural schools generate far less data than large urban schools due to fewer students or less frequent platform use. Traditional AI monitoring techniques often require large, statistically significant data sets to detect meaningful changes. In these cases, limited data volumes poses a challenge, making it harder to identify real issues like model drift, which requires tailored methods that affect work effectively with smaller data sets. By accounting for these unique data distribution challenges like seasonal patterns, evolving student demographics and data volume disparities, we can develop monitoring systems that accurately detect genuine AI performance issues while minimizing false alarms and unnecessary training re uh, retraining cycles. This approach is crucial to building robust adaptive AI systems tailored for educational environments. After understanding the unique challenges and data distribution issues based in educational ML ops, the question arises, how can we overcome these hurdles, especially when technical expertise and resources are limited in many educational institutions. This is where low-code platforms come into play offering a transformative solution. These platforms are transforming how AI is adopted in schools by making the process more accessible, scalable, and collaborative. This is especially important because many educational institute lack the specialized ML engineering resources that typical enterprises may have. Let's explore how these platforms drive democratization and scalability in educational mops. Empowering educators without coding expertise. According to 2022, survey by EdTech Magazine, 60% of schools reported lacking dedicated data scientists, orl engineers, creating a major barrier to AI adoption. How low-code helped these platforms provide drag and drop interfaces and pre-built AI components, enabling educators to deploy AI models like student risk scoring or adaptive learning recommendations without writing code. Example, new term and adapt to learning platform enables teachers to customize content recommendations via in Q2, interfaces boosting student engagement up to 24 5%. In a pilot program, a school district reported that 70% of teachers could configure AI models independently within two weeks of training. In Q2, visual interfaces for configuration and monitoring. Real time MO modeling model monitoring is essential for maintaining accuracy and avoiding false alarms, especially with seasonal or demographic shift in student data. How low-code helps platforms like Microsoft Azure ML Studio offer dashboards that let users at thresholds monitor data drift and receive alerts visually. Example, a school system using Azure ML reduced false positive alerts by 40% by adjusting monitoring thresholds via visual interface, allowing counselors to focus on genuine cases. Educators can tweak parameters such as alert sensitivity in under five minutes without IT support automating complex ML workflows. Traditional level ops pipelines require manual handling of data ingestion, model retraining, validation, and deployment, often needing weeks of effort per update. How low-code helps platforms like Google Vertex AI automate retraining and redeployment when new data arrives? Ensuring models stay current without manual intervention. Example, a pilot with the Midwestern school district saw retraining times cut from two weeks to one day using automated pipelines. This automation led to a 30% increase in model accuracy for predicting student dropout risk. As models incorporated the latest sinister data promptly. Built in governance and compliance, why it matters. Educational data is highly sensitive. Compliance with laws like ferpa, which is Family Educational Rights and Privacy Act in the US or GDPR in Europe, is mandatory. How low code helps platform enable compliance mechanisms like data anonymization, role-based access controls, audit trails directly into the workflows. Example, in a consortium of a hundred plus students, a local platform, ensure a hundred percent compliance with FERPA by automatically masking students' personal identification information. During AI model training and inferences, schools avoided costly fines and reputational risks, saving an estimated 500 K dollars annually in complaints related overhead. Reducing technical barriers and costs. The average cost to hire a full-time ML engineer in the US exceeds one 20 K dollars per year. A prohibitive expense for many schools. How low code helps By minimizing the need for specialized staff and infrastructure schools can implement AI projects at a fraction of the cost. Example, a rural school district within a budget under $1 million implemented predict two analytics for student retention using a low-code platform for less than 15 K dollars annually compared to a traditional bill estimated at over one 50 K dollars. This cost efficiency enabled them to identify at risk students early, improving retention rates by 12% within one academic year, facilitating collaboration between technical. Teams Insights. A 2023 report by the E-D-U-C-A-U-S-C Center found that successful AI deployments in education requires strong collaboration between educators and technical staff. How low-code helps shared platforms with visual tools allow teachers and ML engineers to work in tandem. Teachers provide domain knowledge and context contextual feedback while engineers handle model tuning. Example at a major urban school district, a low-code platform reduced communication gaps, speeding up AI project cycles by 15%. Teachers could adjust model parameters based on classroom observations, leading to 20% higher accuracy in personalized learning recommendations. Local platforms are not just simplifying the low technical side of ai. They are fundamentally demo, demo, democratizing it by making AI accessible, governable and collaborative. These platforms enable educational institutions to leverage data-driven insights effectively and ethically. The result is a scalable. Cost effecti and impactful AI ecosystem that helps educators focus on what matters most improving student outcomes. Now that we have discussed how to design scalable and modular ML ops architectures for education, it's time to talk about the most, one of the most non-negotiable elements of deploying AI in schools, which is the privacy in education. Protecting student data isn't just. Best practice. It's a legal and ethical obligation whether you are working in a public school in the US under Family Educational Rights and Privacy Act called FERPA in Europe, under General Data protection Regulation called GDPR, or in countries like India under the Digital Personal Data Protection Act. D-P-D-P-A. Your ML ops pipelines must be designed with privacy at the core. Data mining pipelines. Let's begin with data minimization. A core principle in privacy law, the idea is very simple. Only use what is essential. For example, if you're training a model to predict students dropout risk, you might not need full names, addresses, or even attendance logs from early years. Many modern ML ops tools now include pre-built data masking and anonymization features. For instance, a development deployment in an Australian school district showed that using a minimized data reduce exposure risk by 40% with no measurable drop in model accuracy. Differential privacy implementation. Differential privacy takes things further by adding statistical noise to the data to prevent re reverse engineering of individual identities. This is especially important in small classroom data sets where there's a higher chance of re-identification. Google's educational AI lab ran a pilot with differential privacy, was applied to student writing analysis. The result. Model performance by only 3.5%, but zero personal traces could be reconstructed even by red team auditors. This is must have for national exams, learning diagnostics, and performance dashboards. Third is a federated learning architectures. Now let's revisit federated learning, but from a privacy first perspective, rather than sending a raw data to the cloud, models are trained locally on school servers or, or even teacher laptops. Only the learn weights, not the data are sent back to the central system. A real world example comes from a Canadian province where 42 schools participated in a federated model to identify learning delays post COVID. This strategy preserved full compliance with local privacy laws while improving predictive accuracy by 16% over a centrally 10 baseline. On device inference prioritization on device inference pushes the privacy boundary even further here, students' data doesn't even leave the device at runtime models, run predictions locally, for example, on a school tablet or a server making it I for behavioral models or assessments. In Brazil, a school network used raspberry pi clusters for local inference on student attention monitoring tools. This architecture allowed predictions to stay entirely within the classroom, ensuring 99% of the data locality even in low bandwidth environments. Conclusion why privacy first ML lops is non negotiable in the enterprise world, privacy features are often add-ons on upgrades. But in education, these are baseline requirements, not optional layers as we move forward, privacy preserving techniques like these must be embedded from the design phase of any ML op workflows deployed in schools. Not only does this protect institutions legally, but it also builds the trust that's essential when students, parents and educators rely on AI to shape academic futures. Now that we have explored how to protect privacy in ML ops pipelines, let's address another critical challenge in educational i ai, which is the bias. Bias in educational AI doesn't. Just affect accuracy. It can amplify existing inequalities from understanding the potential of students from certain backgrounds to misrepresenting learning challenges due to language or age differences. Unfair morals can do real harm. So how do we catch and fix these biases before they impact decisions? Through the automated bias detection mechanisms, which are now being integrated directly into modern ML O pipelines, this can be achieved. Multidimensional fairness metrics in education. Fairness cannot be one dimensional. Models must be evaluated across multiple access, including social, economic status, language proficiency, disability status, and geographic location. For instance, in a New York pilot study, a dropout prediction model showed 12 percentage performance drop of students from English as second language backgrounds. By adding multidimensional fairness checks, the school district corrected this by adding language based feature and retain the model age appropriate evaluation frameworks. Models must be developmentally aware, a 10 year olds learning patterns difference dramatically from those of a 17-year-old. Yet many AI tool use static benchmarks. A good example in Finland bias detection tools flag that reading comprehension models built for older students were misclassifying, anger, anger learners as underperforming due to natural vocabulary limitations after adjusting for age appropriate metrics. False negative rates drop by 22%. Okay. Next is the cultural context. Awareness bias doesn't, does, uh, just come from data. It also comes from cultural misalignment. For example, a model trained in a Western education system might misinterpret student silence as disengagement, whereas in some eastern cultures, they may reflect, respect or contemplation. In India, a sentiment analysis based feedback system had to be retained with regional linguistic nuance dataset after it misread passive positive student responses as disengaged behavior. Local collaboration is essential. What works in Texas might not work in Tokyo, CICD integration for bias testing. Here where ML Ops becomes powerful bias detection shouldn't be a manual after, though it should be part of your ci cd pipeline. Every time a model is retained, train, or pushed to protection, a bias gate should check metrics across all key demographics splits. Microsoft's Fail, learn, and Google's What if tool offer integrations for this? Allowing real time flagging when a model begins to diverge. One university deployment saw violations reduced by 48% within six months just by implementing CI CD based fairness checks. Final message. If you want AI to be a force of for equity in education, we must hardcode fairness into the every stage of the model lifecycle. And thanks to low-code ML ops platforms, these advanced tools, once the domain of AI research labs can now be used by teachers, administrators, and education departments without requiring heavy ML expertise. We have discussed bias, privacy, and democratization in ML ops, but how do we actually structure ML ops to support millions of students across different schools, districts, or even countries with drivers infrastructure? This is where a distributed ML ops architecture tailored for education comes into play. Unlike traditional centralized AI pipelines, educational environments demand a balance of governance, scalability, local autonomy, and data privacy. So let's explore how an adaptable architecture works in this context. This slide outlines a scalable and privacy conscious ML lops architecture, especially designed to meet the unique needs of educational institutions. We are moving from a monolithic cloud only approach to a layered distributed framework that accounts for diverse connectivity levels, privacy mandates, operational flexibility and scalability needs. Here is a layered architecture breakdown, central training infrastructure district or native. This is a centralized brain for of the architecture where AI models are trained, tested, and governed at scale. It acts as a central tower, enduring consistency, compliancy, and reliability. The core functions are aggregates. An anonymized or synthetic student. Data from schools handles training, retraining and version controls. Ensures complaints with loss like used in US GDPR, from Europe, PIP, they from Canada, et cetera. Integrates bio detection, privacy, preserving training and audit logging here is a real world case. The New York City Department of Education runs centralized AI system to analyze student progress and learning gaps across 1.1 million students. They use a custom ML ops platform built on Azure and Databricks to ensure privacy and consistency. Second is the regional deployment hubs, clusters, or province level. These apps as distribution notes that cache models locally and support rapid low latency deployment across geographically group schools. Main core functions are pre-stage updated models and assets, perform local validation before full deployment, and just model slightly for regional content or curriculum differences, and act as intermediate repositories for edge deployment. Benefits are like minimizes van traffic, which is wide area network traffic, enables differentiated rollout, simplifies rollback in case of deployment issues. Here is a real old case scenario In India's thumb, Nado State, a government backed EdTech system uses regional data centers in Chennai and HOR to serve over 30,000 government schools across diverse connectivity landscapes. Each hub handles localized distribution, pushing updates to schools without overlaying central servers. Finally, the school level in inference edge, ai, local devices, AI models are executed on local devices. Example, raspberry by tablets, school servers, minimizing data transfer, and improving privacy and responsiveness. The core functions are performed real time inference for things like student engagement, adaptive learning recommendations, real time alert for learning difficulties, retain raw student data locally. And provide offline support, which is crucial in local connectivity areas. A real world case, uh, use case for this category is in Rwanda. Over 1200 rural schools use Raspberry Pi powered local AI models to assess literacy skills. Results are processed offline and sync weekly when internet is available. This has reduced data exfiltration risk by 85%. This architecture reflects the future of ML ops in education privacy. First, locally adaptive, scalable, and compliant designed for connectivity constraint environments. It empowers governments and schools network to deploy fair, safe, and effective AI at scale, even in resource constraint or remote areas. Let's now explore a crucial component of ML ops in education, containerizing models, serving specifically designed for offline capability. In many educational environments, especially in rural district, low income regions or even urban schools, with aging infrastructure, reliable internet access cannot be taken for granted. This reality makes cloud T AI solutions impractical or inconsistent. So how do we overcome this? The answer lies in containerization, a modern software packaging strategy that allows us to bundle AI models, runtime environments, and dependence into a self-contained unit that can run reliably on premise even without internet access. In educational ML ops, containerized AI applications allow localized, secure, and consistent delivery of AI services directly on school infrastructure, such as teacher laptops, school servers, or student tablets. Here on the key implementation strategies, lightweight container images, optimize it for educational hardware. The first pillar is minimizing the weight of containers. Many schools still run on low spec hardware, vehicle lightweight container images, often under one 50 mp. That can still deliver high value a functions like grading, tutoring, or content recommendation. For example, a reading assessment model was compressed using OS and QUANTIZE to eight bit precision, allowing it to run smoothly on a raspberry PI four with two GB ran. Progressive model loading. Not all components of an AI model needs to load at once. With progressive loading, we can prioritize loading critical model components first, such as coding or classification layers, and then gradually load personalization modules or visualizations if and when resources are available. Think of it like an AI that gets smarter the longer it turns, but never blocks learning even with minimal capability. In intelligence synchronization protocols, when connectivity does return, we use synchronization protocols that are smart and secure. These protocols, buffer data, locally encrypted and attempt transition transmission only when stable connectivity is detected. This ensures no student data is lost, even if the device goes offline for days or weeks. In Ghana's Eastern Pilot Project schools with only four hours of internet per week, still managed to sync over 93% of data logs using Delta updates and RESUMABLE protocols like syc. Local data caching with privacy, preserving encryption. We don't just cache data, we do it securely. Every interaction, score, or submission is stored locally in encrypted form, often using a ES 2 56 or lightweight homomorphic encryption methods. This protects sensitive educational data even in shared classroom devices. Graceful degradation pathways. Finally, what happens if the AI model fails to load fully due to power issues, hardware crashes, or memory constraints? We implement graceful degradation where the system defaults to predefined rules, cache outputs, or static learning content, ensuring the students experience is disrupted as little as possible. A practical example, if you're writing feedback model crashes, the system can fall back to where Rubik based scoring guide already embedded in the app. To summarize containerized modeling, serving is not just a DevOps convenience. It's a strategic imperative for ensuring that AI tools in education are accessible, resilient, and equitable, especially in connecting connectivity constraint environments. And as we scale AI in education, this approach will be the backbone for enabling consistency, privacy, and functionality regardless of a school's internet bandwidth. Now that we have discussed deployment and offline capabilities, let's focus on a, on a critical lifestyle component for any AI system in education. Automated retraining, unlike tactic software, AI model, lean from learn from data, but they also become outdated over time. In education model, drift can be caused by academic calendars, curriculum changes, seasonal learning behaviors, or even localized student progress PA patterns. So how do we ensure our AI system remain accurate, fair, and effective over time? The answer lies in building automated retraining pipelines, customized for the operational and ethical demands of educational environments. The cyclical nature of retraining pipelines as shown in the graphic. The retraining process follows a continuous loop that allows us to refresh our models in a privacy preserving context, sensitive and scalable way. Let me walk you through each phase of this pipeline. The first phase is the scheduled data collection. It all starts with. Schedule data collection at regular intervals, say monthly or quarterly. We aggregate anonymized model performance logs from school infrastructure. These logs include accuracy of predictions, confidence levels, engagement metrics, edge case flags. Importantly, this process must be compliant with data privacy laws like FE, GDPR, or India's DPDP Act, depending on where the schools are. We use differential privacy or federated integration techniques so that student identifies never leave local devices. Next comes distribution analysis where we compare current data distribution to those from previous cycles. For instance, a model train in February may begin underperforming in June due to exam stress, summer learning loss, or changes in students' engagement. Drift Detection in education is complex because you need to account for seasonality and economic performance. Separate natural learning progression from true model drift, detect regional disparities in how AI is used or interpreted. This is where education specific drift detection frameworks become essential, not just traditional K tests or care divergence. The next step in the. Framework is the contextual retraining, rather than retraining the entire model, we use contextual retraining approaches, which means we target only on the underperforming components. Example, feedback modules, prediction heads focus on effective student segments. Example, grade five, ESL, learnings in roller clusters. This saves compute, avoids over. Fitting and ensure more localized and equitable updates. For example, in one district pilot, only 11% of the models parameters had to be updated to correct bias against neuro divergent learners. Multidimensional validation. Once retrained, the module undergoes multidimensional validation. This testing isn't just about accuracy. We look at fairness across gender, linguistic background, disability status, interpretability metrics. Example, it's a model's feedback, actionable bias, or its using synthetic counterfactual data. A retrained model that performs well only for urban students, but degrades for rural schools is immediately flagged in real world deployments. Models that pass five plus geo, geo, uh, demographic, uh, validations were found to main gain up to 97% Fairness, parity in public school settings, graduated deployment. Finally, we don't roll out changes instantly. We use grad, graduated deployment. Start with pilot schools, monitor outcomes for two, three weeks. Get feedback from educators and school admins, then roll out to the broader district or region. This prevents mass failure and ensures human oversight at each step. Why this matters in education, unlike tech tech companies that can retain models nightly with millions of realtime users, school operates on slower structural timelines. You must respect the economic year. Student stability and policy constraints. These retaining pipelines balance automation with caution, ensuring that AI tool remains relevant and useful updates don't introduce new bias. The system evolves with students not ahead of them. In short, automated retraining pipelines make educational AI systems adaptive, safe, and sustainable. They help us avoid model dk, uphold ethical standards, and deliver consistent personalized support to students, whether in a major urban school or a remote classroom in the mountains. As we move from retraining pipelines, let's now look at how large scale collaboration across multiple schools can be made, both powerful and privacy respecting through federated learning. Unlike traditional AI models that rely on sending raw data to the cloud, federated learning flips the script. The model goes to the data instead of the data going to the model. What is Federated Learning in Education? Federated Learning is a decentralized machine learning approach where local models are trained on individual school devices and only model updates, not student data, are shared back to the central system. This is especially, especially crucial in education because schools are spread across district with different resources, bandwidths and IT policies. Students data is highly sensitive and centralizing. It creates compliance risk in rural and underserved regions. Connectivity is sporadic, but learning must continue. Let's break down how it works in practice. Local model training on school devices. Each school trains its version on model locally using only the student data available within its premises. We a smart tablet or a school server or a classroom AI assistant. For example, a middle school in a tier three city can train a reading comprehension model using their own data on device, capturing local essence, reading patterns and curriculum specific nuances. Encrypted parameter sharing one. Local training completes. Instead of sending raw data, each school sends only encrypted model updates to the district coordination server. These updates could be gradient vectors, weight, weight changes, performance deltas all transmissions use end-to-end encryption, such as TLS 1.3, and a S 2 56, and often pass through zero trust architecture, ensuring the process is tamper resistant and auditable. Differential private receive integration to award the risk of reverse engineering student information from gradients, which is possible. In NA federated setups, we implement differential privacy mechanisms. This involves adding calibrated noise to model updates so that no single student contribution can be identified. Even if a malicious actor access the updates, they would learn nothing individual or identifiable. This satisfies legal mandates in GDPR Article 25, which is Privacy by Design, F-E-R-P-A, which is Student Educational Records protection, and India's D-P-D-P-A 2023, which is local storage and processing of personal data. Next is the adapt to AG aggregation, strateg. Once updates from all the schools are received, the central server does not average them equally. Instead, we use adapt to aggregation, which takes into account, vary school sizes, demographic diversities, update trustworthiness. These strategies ensure fair representation across districts. Low-code in interface for simplicity. To make federated learning accessible to non-technical educators, we use low-code platforms like automate training, job triggers, visualize model performance for per school, provide compliance ready logs require no advanced DevOps or ML expertise. This is critical for scaling federated learning in real world school system, many of which lack full-time data science staff. To sum up, federated learning is not just a technical innovation, it's a governance breakthrough. It allows us to honor privacy laws, leverage local intelligence, and enable district-wide learning, progresses through collaborative intelligence, not centralized control. Up to this point, we have talked about architectures, privacy deployment strategies for AI in education. But an equivalent important pillar of ML Ops is monitoring, making sure our models and systems continue to perform as expected once they're in real world use. Now in the most enterprise environments, monitoring is somewhat easier because the devices are relatively standardized. Servers in data centers or companies show laptops with similar configurations. Education is very different. Schools operate on on shoestring budgets, often relying on a mix of newer devices, older desktop, shared lab machines, and even students' personal devices. This creates what we call the heterogeneous device ecosystem. Why does that matter? Because traditional one size fits all, monitoring does not work here. If we push enterprise grade monitoring tools onto low end machines, they will struggle just to keep up. On the other hand, if we only apply lightweight monitoring everywhere, we lose the deep visibility needed for high performance systems. So the real challenging is finding a balance, designing, monitoring strategies that respect device diversity, keep system relatable and remain accessible to non-technical stakeholders like teachers and administrators. On this slide, I will walk you through four key strategies that make monitoring both practical and inclusive in educational environments. Resource aware metrics collection, what it means, monitoring systems adapt to the capability of each device. And how it works. High-end servers can collect detailed metrics, while older laptops may only track essentials like uptime and memory usage. Example, in a rural school with decade old desktops, only basic health checks run, so learning apps remains usable. Meanwhile, an urban school with newer hardware track advanced telemetry like GP utilization for AI driven tutoring apps, device specific performance baselines. What it means, each device type has its own normal performance thresholds and how it works. Instead of using one benchmark across all devices, the system knows that a Chromebook should not be expected to run at the same speed as a lab workstation. Example, a math learning app runs slower on a low cost tablet, but that's expected. The baseline is adjusted to avoid constant false alarms. At the same time. If the same slowdown happens on a powerful lab pc, the system flag, it is a real issue. Centralized monitoring dashboards what it means. Complex monitoring data is transformed into simple visual insights through low-code dashboards and how it works. Teachers or administrators see color-coded health indicators, trend lives or alerts of roll logs. And an example for this, a district admin logs into a dashboard and sees school is devices are healthy, but school based hitting memory limits on older laptops. This empowers them to act maybe by shifting workloads or prior prioritizing an upgrade without needing deep technical expertise. And finally, automated intervention works. Flows. What does this mean? The system can respond automatically to detect tissues, reducing downtime and dependency on it staff and how it works. Predefined rule, trigger actions like rolling back a buggy model, reducing batch sizes, or switching to offline cache data. Example for this. If a school's internet drops, the AI app automatically switches to offline inference mode. Or if a new model starts slowing down old machines, the system auto automatically reverts to a previous table version until it reviews it. These four strategies, adaptive metrics, tailored baselines, educator friendly dashboards, and automated interventions together ensure that monitoring isn't just technically effective, but also practical. In resource constraints schools, the goal is resilience, keeping AI systems running smoothly, even in S and unpredictable classroom environments. We have looked at architectures and monitoring, but a key question many institutions have is, how do we actually get started? Implementing ML ops in education isn't a one step process. It needs to be staged carefully because schools have limited resources wearing technical skills and strict privacy requirements. This roadmap breaks the journey into four clear phases, foundation. Scale and optimization. Each phase builds on the last. Ensuring institutions can start small, prove value, and then expand sustainably without overwhelming their teams or budgets. Phase one, the foundation, what it is, setting up the initial low-code ml, lops platform privacy frameworks and training. This focuses on building a secure foundation before scaling. And in the example for this, a districts in a low-code AI platform that trains a small group of educators and IT staff to manage deployments. Privacy measures like differential privacy and access controls are ap, uh, are established upfront. The phase second phase is the pilot deployment. What it is running small scale pilots in selected schools with different resource profiles. This focuses on testing containerized, model serving basic monitoring and governance workflows. And an example for this is a rural school test. Offline ready AI models to handle poor internet via an urban school test. Federated learning for scaling the pilot reveals practical constraints like certain devices needing lighter models. The third is a scaled rollout. What it is, expanding deployment district-wide with localized adaptations. This focuses on standardizing governance, integrated federated learning for collaboration across schools. And example, once pilots succeed, the district rolls out AI assisted learning apps to all schools. Models are turned differently depending on bandwidth, but all share updates through federated learning. And the final stage is the optimization phase. This is the continuous improvement through retraining and education specific monitoring and focuses on fine tuning, automated retraining pipelines and dashboards. Example, the system detects that match models underperformed for younger students. A target retraining cycle is launched and dashboards are updated to highlight grade level performance differences for administrators. This phase roadmap ensures institutions do not try to boil the ocean from day one. Instead, delay a strong foundation, test and control pilots expand in a managed way, and then continuously optimize This approach delivers immediate wins while building toward long-term scalable AI adoption in education. Up until now, we have explored challenges, architectures monitoring, and the roadmap for rolling out ML lops in schools. But at the end of the day, the most important question people ask is, does this really work in real world? This slide captures the outcomes we have seen when districts adopted these approaches. What's powerful here is that the results span technical reliability, cost efficiency, and human engagement. In other words, it's not just about keeping AI running, it's about keeping teachers and students engaged while saving schools money and staying compliant and with privacy regulations offline real reliability. What does this mean? AI tools continue functioning even during internet disruptions and why it matters. Many schools, especially rural ones, face bandwidth issues. An example, a literacy is assist app in a rural district kept working seamlessly during outages because of contain, uh, containerized offline inference, ensuring students didn't lose learning time. Resource utilization, what it means? Optimized edge deployment reduces computing loads and costs. And why does it matter? Schools operate on tight budgets with limited hardware. An example, by using lightweight containerized models, one district reduces server costs and extended the usable life of older laptops by two years. The next one is a non-technical engagement. What does it mean? Educators and administrators actively participate in AI governance through low-code tools and why it matters without engaging educators. AI remains a black box and risk low adoption. Example, teachers use a dashboard to tweak content delivery thresholds without calling it directly improving classrooms engagement. Implementation impact AI tools scaled across multiple districts serving millions of students. Consistency, maintained model performance in bandwidth constrained environments. Teachers adjusted AI recommendations without coding regulatory compliance. Met privacy regulations across regions using federated learning and differential privacy. Reduce the technical burden, self-healing deployment architectures maintain it interventions. These results prove that when we design ML lops for education with low-code privacy, offline capability, and inclusivity, it's not just theory. It leads to a real world impact keeping students learning, saving schools money, empowering teachers, and ensuring compliance at scale. Now we have. We have now walked through the educational ML ops landscape, the challenges schools face, the architectures that can work, and even real world results from implementations. But I know that in a fast moving conference setting like this, it's easy to get lost in the detail. So I want to step back and highlight the three key takeaways that really capture the essence of the entire talk. These are the principles that should guide everyone. Anyone trying to scale AI responsibly in education? Think of them as a north star, simple, practical, and non-negotiable. If schools or districts follow these, they won't just deploy a, they'll deploy AI that's sustainable, equitable, and trusted by educators and students alike. Low-code empowers education. One of the most important points is that low-code platforms break down technical barriers. Instead of relying solely on machine learning engineers, which schools rarely have teachers and administrations can participate directly in AI deployment and governance. This democratization makes adoption much more feasible In education. Example, a teacher adjusts content delivery thresholds in a dashboard, improving students' engagement without any coding. Privacy first by design In education, privacy is not optional. It's mandatory by integrating federated learning, differential privacy and data minimization, pipelines From the very beginning, schools can protect sensitive student data while still leveraging AI insight. Example, instead of sending raw student data to the cloud, only encrypted model updates are shared across schools. And finally, offline Functionality is key. Connectivity can be taken for granted, especially in rural or underserved districts. Containerized serving with local inference ensures AI tools keep working even without reliable internet. This isn't just a nice to have, it's a critical requirement for equity in education. Example, a rural schools AI tutoring system continues guiding students during an internet outage, syncing updates later when the network returns. So to sum it up, AI and education must be inclusive. Privacy first and T local code EM empowers educators to take part privacy, safeguard, build trust, and offering capabilities, ensure no student is left behind. If we design with these principles, AI can truly scale across education in a way that is sustainable, far and impactful. That brings me to the end of my talk. I want to sincerely thank you all for your time and attention today. Educational ML Ops is not just a technical challenge. It's about making AI accessible, equitable, and sustainable for teachers and students who need it to the most. My hope is that you leave here with a few ideas on how local platforms, privacy first design, and often functionality can come together to truly, truly democratize AI in education. I would also like to thank conference 42 for creating this platform to share ideas and all of you for thoughtful discussion and innovations you will take forward from here. Thank you, and I would be happy to take any questions.