Data-Driven Training Approaches

Training a Large Language Model (LLM) involves more than just scaling up data and compute. It requires a disciplined approach across multiple layers of the ML lifecycle to ensure performance, efficiency, safety, and adaptability. This visual framework outlines eight critical pillars necessary for successful LLM training, each with a defined workflow to guide implementation: 𝟭. 𝗛𝗶𝗴𝗵-𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗗𝗮𝘁𝗮 𝗖𝘂𝗿𝗮𝘁𝗶𝗼𝗻: Use diverse, clean, and domain-relevant datasets. Deduplicate, normalize, filter low-quality samples, and tokenize effectively before formatting for training. 𝟮. 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Design efficient preprocessing pipelines—tokenization consistency, padding, caching, and batch streaming to GPU must be optimized for scale. 𝟯. 𝗠𝗼𝗱𝗲𝗹 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗗𝗲𝘀𝗶𝗴𝗻: Select architectures based on task requirements. Configure embeddings, attention heads, and regularization, and then conduct mock tests to validate the architectural choices. 𝟰. 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗦𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 and 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Ensure convergence using techniques such as FP16 precision, gradient clipping, batch size tuning, and adaptive learning rate scheduling. Loss monitoring and checkpointing are crucial for long-running processes. 𝟱. 𝗖𝗼𝗺𝗽𝘂𝘁𝗲 & 𝗠𝗲𝗺𝗼𝗿𝘆 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Leverage distributed training, efficient attention mechanisms, and pipeline parallelism. Profile usage, compress checkpoints, and enable auto-resume for robustness. 𝟲. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 & 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻: Regularly evaluate using defined metrics and baseline comparisons. Test with few-shot prompts, review model outputs, and track performance metrics to prevent drift and overfitting. 𝟳. 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗮𝗻𝗱 𝗦𝗮𝗳𝗲𝘁𝘆 𝗖𝗵𝗲𝗰𝗸𝘀: Mitigate model risks by applying adversarial testing, output filtering, decoding constraints, and incorporating user feedback. Audit results to ensure responsible outputs. 🔸 𝟴. 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴 & 𝗗𝗼𝗺𝗮𝗶𝗻 𝗔𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻: Adapt models for specific domains using techniques like LoRA/PEFT and controlled learning rates. Monitor overfitting, evaluate continuously, and deploy with confidence. These principles form a unified blueprint for building robust, efficient, and production-ready LLMs—whether training from scratch or adapting pre-trained models.

The DOJ consistently says that compliance programs should be effective, data-driven, and focused on whether employees are actually learning. Yet... The standard training "data" is literally just completion data! Imagine if I asked a revenue leader how their sales team was doing and the leader said, "100% of our sales reps came to work today." I'd be furious! How can I assess effectiveness if all I have is an attendance list? Compliance leaders I chat with want to move to a data-driven approach but change management is hard, especially with clunky tech. Plus, it's tricky to know where to start– you often can't go from 0 to 60 in a quarter. In case this serves as inspiration, here are a few things Ethena customers are doing to make their compliance programs data-driven and learning-focused: 1. Employee-driven learning: One customer is asking, at the beginning of their code of conduct training, "Which topic do you want to learn more about?" and then offering a list. Employees get different training based on their selection...and no, "No training pls!" is not an option. The compliance team gets to see what issues are top of mind and then they can focus on those topics throughout the year. 2. Targeted training: Another customer is asking, "How confident are you raising bribery concerns in your team," and then analyzing the data based on department and country. They've identified the top 10 teams they are focusing their ABAC training and communications on, because prioritization is key. You don't need to move from the traditional, completion-focused model to a data-driven program all at once. But take incremental steps to layer on data that surfaces risks and lets you prioritize your efforts. And your vendor should be your thought partner, not the obstacle, in this journey! I've seen Ethena's team work magic in terms of navigating concerns like PII and LMS limitations – it can be done!

𝐓𝐡𝐞 𝐒𝐞𝐜𝐫𝐞𝐭 𝐭𝐨 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐓𝐡𝐚𝐭 𝐀𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐖𝐨𝐫𝐤𝐬? 𝐒𝐭𝐚𝐫𝐭 𝐚𝐭 𝐭𝐡𝐞 𝐄𝐧𝐝. 🏁 I used to think my job as an L&D professional started with a syllabus. I was wrong. Recently, I was tasked with building a learning solution for our Talent Acquisition (TA) team. The goal wasn’t just to "train recruiters"—it was to solve a business problem. Instead of looking at what they needed to know (Level 2), I started with what the business needed to achieve (Kirkpatrick Level 4). The "Reverse" Approach I didn’t start with slides. I started by analyzing Voice of the Customer (VOC) survey results, focusing on various metrics from both Hiring Managers and Candidates. Working Backwards: ✅ Level 4 (Results): I defined the business KPI. ✅ Level 3 (Behavior): Based on the VOC metrics, I identified the specific actions recruiters needed to change—specifically around "Precision Intake" and "Candidate Experience Management." ✅ Level 2 & 1 (Learning & Reaction): Only then did I design the actual training content that addressed those specific behavior gaps. The Result? The training didn't feel like a chore; it felt like a solution. Because I built it based on the actual metrics revealed in the VOC surveys, the TA team saw immediate value, and the business saw a measurable shift in hiring efficiency. The Lesson: If you want your learning solutions to be more than just "check-the-box" exercises, stop asking "What should we teach?" and start asking "What does the data say I need to solve?" How do you use VOC data to shape your enablement programs? 👇 #LearningAndDevelopment #InstructionalDesign #TalentAcquisition #KirkpatrickModel #Enablement #DataDrivenLD #BusinessImpact

From chatbots that personalize microlearning to systems that predict who’s likely to disengage, artificial intelligence (AI) is changing how we train and learn. AI opens new opportunities to improve on some of the challenges with traditional training models such as scalability, personalization and real-time feedback. Core AI applications in the L&D space can be broken down into four categories: Artificial Intelligence (AI) Platforms: These tools tailor difficulty, pacing and topics in real time. An AI-enhanced platform can tailor the content to the learner based on their performance trends. Natural Language Tools: These are used to summarize content, create quizzes and provide conversational coaching. These applications can reduce time spent on administrative tasks and increase the focus on building relationships and delivering value. Predictive Analytics: This category of tools help learning leaders identify skills gaps and forecast learner success. Virtual Coaches and Chatbots: These tools reinforce knowledge through spaced repetition and feedback loops. AI-Powered Learning: A Case Study Streamline Services is a fifth-generation plumbing, electrical and HVAC company that handles up to 200 calls a day and serves thousands of customers each month. The company is using AI to not only coach employees but also identify areas where the team needs skills development or training. Streamline adopted an AI-powered virtual ride along platform to help transform everyday customer interactions — both in the field and in the call center — into powerful, data-driven learning opportunities. Traditionally, managers and trainers could only coach based on a handful of ride alongs or recorded calls each month. With AI, every service visit and customer conversation has become searchable, analyzable and coachable. AI highlights key themes including customer concerns, missed opportunities and tone shifts, allowing trainers to see real patterns instead of isolated incidents. The training team and managers use this knowledge to design training and structure coaching for individual needs. Because AI is deepening Streamline’s understanding of customer needs, the L&D team can develop targeted training that improves customer service and empathy across the company. Streamline’s experience illustrates how AI is fundamentally changing the learning process — from reactive coaching based on limited observation to proactive, personalized development powered by real data. This case study showcases how technology can elevate human performance rather than replace it. AI offers the ability to provide more learning opportunities and personalized learning across roles and industries. L&D professionals need to embrace this change and evolve alongside the technology. The future of learning isn’t artificial — it’s intelligently human. #LearningandDevelopment #AI #FutureofLearning

🚀 DD-FEM: Train Small, Model Big (Local Training → Global Assembly) What if we could learn physics locally—on small patches—then assemble those learned “data-driven elements” to solve much larger PDE problems without retraining? That’s the idea behind DD-FEM (Data-Driven Finite Element Method): ✅ Locally trained, data-driven basis functions ✅ Finite-element-style local-to-global assembly ✅ Governing equations still enforced (not a black box) Why it matters (results we’re excited about): + >1000× speedup with <1% relative error on lattice-type elasticity—showing globally accurate solutions assembled from small, locally trained components. + 23.7× speedup with <4% error for scaled-up steady Navier–Stokes porous-media flow using DG-style coupling.  + 662× speedup with ~1% error for time-dependent Burgers dynamics, while generalizing across space/time from local training.  + A single learned manifold can represent both Poisson and Burgers, trained on local 2×2 subdomains (4,000 Poisson + 101,000 Burgers snapshots). The big picture: DD-FEM keeps the numerical-method rigor and modularity we trust—while gaining the reuse and scalability we want from data-driven models. 📄 If you’re curious about the framework and results, see our paper “Defining Foundation Models for Computational Science: A Call for Clarity and Rigor.” + Link to the paper: https://lnkd.in/gWSHPAqj #SciML #Numerical #Methods #Finite #Elements #Model #Reduction #Domain #Decomposition #HPC #PDE #Foundation #Models #libROM

Training a machine learning model isn’t just about feeding data into an algorithm. It’s a structured journey - starting from clearly defining the problem, preparing the data, choosing the right model, and finally deploying it into the real world. Each step plays a crucial role in ensuring the model learns effectively, performs reliably, and continues to provide value even after deployment. Here’s a complete breakdown of the end-to-end process in a clear, easy-to-understand sequence: 1. Define the Problem Clarify what you want the model to solve and the business objective behind it. 2. Collect & Prepare the Data Gather relevant data, verify quality, and label it correctly if it’s supervised learning. 3. Explore & Analyze the Data Understand patterns, correlations, missing values, and trends through exploratory analysis. 4. Preprocess the Data Clean, transform, and normalize the data so the model can learn effectively. 5. Select a Model Choose algorithms that fit the problem—like decision trees, SVMs, or neural networks. 6. Train the Model Feed the training data, tune hyperparameters, and validate performance during training. 7. Optimize the Model Fix underfitting or overfitting by adjusting hyperparameters or improving features. 8. Evaluate the Model Test the model using metrics like accuracy, precision, recall, and F1-score. 9. Deploy the Model Convert the trained model into a production-ready format and integrate it into applications. 10. Monitor & Maintain the Model Track performance, handle data drift, and update the model as real-world data evolves. Training a machine learning model isn’t magic - it’s a structured journey of defining, exploring, building, testing, and improving. Master these steps, and you’ll understand not just how ML works, but why each stage matters. If you want more breakdowns that simplify complex tech like this, stay connected - more coming your way.

42.1% error reduction with 85% less data. At Ento, we use a lot of traditional black-box Machine Learning to model building energy consumption, and they're great for many use cases. But they have their limits. When we're dealing with: - Plenty of indoor sensor data - Limited historical data - The need to actively control a building’s HVAC system ... plain black-box approaches often fall short. That’s why I’ve been following key trends around blending data-driven methods with physical modeling: 🔹 Transfer Learning: Use data from similar buildings to improve models. 🔹 Digital Twins: Blend data-driven methods and physical simulations. 🔹 Physics-Informed AI: Embed physical laws into the learning process to improve results. Just last month, three papers in these fields came out from leading researchers: - GenTL: A universal model, pretrained on 450 building archetypes, achieved a 42.1% average error reduction when fine-tuned with 85% less data. From Fabian Raisch et al. - An Open Digital Twin Platform: Han Li and Tianzhen Hong from LBNL built a modular platform that fuses live sensor data, weather feeds, and physics-based EnergyPlus models. - Physics-informed modeling: A new study proved that Kolmogorov–Arnold Networks (KANs) can rediscover fundamental heat transfer equations. From Xia Chen et al. Which of these 3 trends do you see having the biggest real-world impact in the next 2-3 years?

Are you aware of "Teacher Hacking"? (Full disclosure – Despite my daughter's suspicions, this post is not a guide on how to hack your teacher… or your kid’s.) If you're building an LLM or using one for your applications, you should keep reading. There are several techniques for developing LLMs to enhance their accuracy and safety. Two common methods are: → 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗗𝗶𝘀𝘁𝗶𝗹𝗹𝗮𝘁𝗶𝗼𝗻: Training a model to mimic a more advanced teacher model. → 𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝗛𝘂𝗺𝗮𝗻 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 (𝗥𝗟𝗛𝗙): Using human feedback to align a model's responses with desired behaviors. Researchers often express concerns about 𝗥𝗟𝗛𝗙 overfitting, where a model "reward hacks" its way to over-optimize on the training data, leading to poor performance on new tasks. A less known but more sneaky issue is "teacher hacking" during the 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗗𝗶𝘀𝘁𝗶𝗹𝗹𝗮𝘁𝗶𝗼𝗻 process. This occurs when a student model, trained by imitating a teacher model, learns not only the teacher's strengths but also its errors and biases - especially when the training data is static and limited. So, while this is a very cool and efficient method, there is a catch. Recent examples such as 𝗭𝗲𝗽𝗵𝘆𝗿, 𝗚𝗲𝗺𝗺𝗮-𝟮, 𝗮𝗻𝗱 𝗗𝗲𝗲𝗽𝗦𝗲𝗲𝗸-𝗩𝟯 demonstrate the growing popularity of distillation techniques. However, a new study (𝘭𝘪𝘯𝘬 𝘪𝘯 𝘵𝘩𝘦 𝘧𝘪𝘳𝘴𝘵 𝘤𝘰𝘮𝘮𝘦𝘯𝘵) warns that without careful attention to the training process, teacher hacking can significantly degrade model performance. A key strategy to counteract teacher hacking is data diversity. Relying solely on outdated or narrowly sourced data is risky. Instead, it is critical to train models on fresh data drawn from a wide range of sources. This approach ensures that the model: → 𝗔𝗱𝗮𝗽𝘁𝘀 𝘁𝗼 𝗡𝗲𝘄 𝗧𝗿𝗲𝗻𝗱𝘀: By continuously integrating current data, the model stays up-to-date with emerging trends and evolving global contexts. → 𝗖𝗮𝗽𝘁𝘂𝗿𝗲𝘀 𝗗𝗶𝘃𝗲𝗿𝘀𝗲 𝗣𝗲𝗿𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲𝘀: Exposure to varied sources minimizes the risk of replicating a single source's errors or biases, enabling more balanced and nuanced understanding. → 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝘀 𝗥𝗼𝗯𝘂𝘀𝘁𝗻𝗲𝘀𝘀: A constantly updated and diverse dataset helps the model develop independent reasoning, making it more resilient and adaptable in an ever-changing world. For the next generation of language models to excel, integrating a steady stream of fresh, varied data - especially around security and safety - is not just beneficial - it is essential. This strategy prevents the propagation of errors and biases, enabling developers to build robust, efficient, and adaptable models capable of performing accurately across a wide spectrum of tasks.

In our comprehensive guide, the team at Datature detail how we trained state-of-the-art architectures - like 3D U-Net (#SwinUNET) - on complex volumetric datasets (CT, MRI, and more). We outline the training workflow on the #BraTS dataset, including preprocessing steps, data augmentations to address rotation and scale invariance issues, and the computational challenges posed by high-resolution 3D data. Strategies such as patch-based training, gradient checkpointing, and mixed precision training are discussed to mitigate memory constraints. Our technical deep dive shares the experimental setups, challenges, and lessons learned that can drive superior segmentation performance in clinical applications. Read More Here → https://lnkd.in/gVjUuW5H

"I can tell they're improving" used to be my go-to coaching feedback. Until I saw the data that proved me wrong 73% of the time. Here's what happens when you let data drive your coaching: Your "strongest" rep might actually be losing the most winnable deals. Numbers don't lie - even when our instincts do. I discovered this when analyzing both call scores & conversion rates: The "natural closer" was bottom quartile in early discovery calls. The "struggling" rep? Top performer in demo-to-close. This changed everything about our coaching approach: • Replaced "great job" with "your qualification score increased 22%" • Swapped "needs improvement" with "here's where you're losing momentum" • Started tracking call quality scores in each sales stage The result? Rep performance feedback became unquestionable. Coaching conversations transformed from defensive to collaborative. Revenue increased 31% in 6 months. Not because we worked harder. Because we finally knew exactly what to work on. Your gut feeling might be good. But data-driven coaching is transformational. What metrics are you tracking in your coaching sessions?