Continuous Learning Practices

ML/LLMOps fundamentals - 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 (𝗖𝗧) and what steps are needed to achieve it. CT is the process of automated ML Model retraining in Production Environments on a specific trigger. Let’s look into some prerequisites for this: 1) Automation of ML Pipelines. - Pipelines are orchestrated. - Each pipeline step is developed independently and is able to run on different technology stacks. - Pipelines are treated as a code artifact. ✅ You deploy Pipelines instead of Model Artifacts allowing Continuous Training In production. ✅ Reuse of components allows for rapid experimentation. 2) Introduction of strict Data and Model Validation steps in the ML Pipeline. - Data is validated before training the Model. If inconsistencies are found - Pipeline is aborted. - Model is validated after training. Only after it passes the validation is it handed over for deployment. ✅ Short circuits of the Pipeline allow for safe CT in production. 3) Introduction of ML Metadata Store. - Any Metadata related to ML artifact creation is tracked here. - We also track performance of the ML Model. ✅ Experiments become reproducible and comparable between each other. ✅ Model Registry acts as glue between training and deployment pipelines. 4) Different Pipeline triggers in production. - Ad-hoc. - Cron. - Reactive to Metrics produced in Model Monitoring System. - Arrival of New Data. ✅ This is where the Continuous Training is actually triggered. 5) Introduction of Feature Store (Optional). - Avoid work duplication when defining features. - Reduce risk of Training/Serving Skew. 𝗠𝘆 𝘁𝗵𝗼𝘂𝗴𝗵𝘁𝘀 𝗼𝗻 𝗖𝗧: ➡️ Introduction of CT is not straightforward and you should approach it iteratively. The following could be good Quarterly Goals to set: - Experiment Tracking is extremely important at any level of ML Maturity and the least invasive in the process of ML Model training - I would start with ML Metadata Store introduction. - Orchestration of ML Pipelines is always a good idea, there are many tools supporting this (Airflow, Kubeflow, VertexAI etc.). If you are not doing it yet - grab this next, also make the validation steps part of this goal. - The need for a Feature Store will wary on the types of Models you are deploying. I would prioritise it if you have Models that perform Online predictions as it will help with avoiding Training/Serving Skew. - Don’t rush with Automated retraining. Ad-hoc and on-schedule will bring you a long way. Let me know your thoughts! 👇 #LLM #MachineLearning #AI ---------- Be sure to ♻️ repost if you found the article useful and follow Aurimas if you want to get a daily dose of useful AI related content in your feed!

DSA Roadmap for 2025 Don't begin with Leetcode blindly. Instead, follow a path that actually builds confidence + pattern recognition 🔹 Step 1: Pick ONE Language Forget switching between C++, Java, Python. Just pick one and go deep. Start with mastering: • Arrays (reverse, rotate, max sum) • Strings (palindrome, anagram check) • HashMaps (freq count, two sum) 🔹 Step 2: Build Confidence (10–15 Easy Problems) Before diving into hard stuff, nail the basics: • Reverse a string • Max/min in array • Frequency of characters • Find duplicates Goal: Build logic + problem-reading ability 🔹 Step 3: Learn Core Patterns, Not Problems Most tech interviews use 20–25 repeatable patterns. Here are a few you must master: • Sliding Window → Longest Substring w/o Repeating • Two Pointers → 3Sum, Container With Most Water • Binary Search → Search in Rotated Array, Peak Element • Recursion + Backtracking → N-Queens, Subset Sum • Graph Traversals → Flood Fill, Cycle Detection • Dynamic Programming (DP) → Fibonacci, Knapsack, Longest Palindromic Substring Learn: when to apply, how to identify, and how to code from scratch. 🔹 Step 4: Mediums (Intentional Struggle) This is your growth phase. Struggle is part of learning. ✔️ Build debugging muscle ✔️ Practice writing from scratch ✔️ Explain your solution aloud ✔️ Don’t copy answers — fight with the logic Tip: Mediums are where your resume gets built. 🔹 Step 5: Pressure Simulation DSA is not just about solving — it’s about solving under time + stress. Weekly habit: • Pick 3 problems from different topics • Set a 90-minute timer • No tab switching, no breaks Build interview stamina. 🔹 Step 6: Revisit & Revise in Cycles Just solving once won’t cut it. Every week: Pick 1–2 patterns you’ve done before • Solve 2 new problems in each • Use a whiteboard or paper sometimes (for real feel) This builds retention and intuition. 🔹 Step 7: Focus on Real-World Applications DSA is not just for interviews. It’s for systems, scalable apps, and optimizations. Start thinking: • Heap vs Trie — when & why? • BFS or DFS in shortest path? • DP or Greedy — trade-offs? Blend it with System Design once you’re confident. Tips (Keep These in Mind): ✅ Don’t stick to one problem too long — switch, revisit, and learn the pattern. ✅ Track by patterns, not platforms — use Notion or GitHub. ✅ Teach 1 concept/week — it sharpens your understanding. ✅ Focus on clarity over count — depth beats quantity. ✅ Start mock interviews early — readiness comes with practice. You need the right patterns, in the right order, with the right focus. That’s what I’ve built for you: ✅ Notes that are concise and clear ✅ Most-asked questions per topic ✅ Real patterns + approaches to master them 👉 Grab the DSA Guide → https://lnkd.in/d8fbNtNv 𝐅𝐨𝐫 𝐌𝐨𝐫𝐞 𝐃𝐞𝐯 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐉𝐨𝐢𝐧 𝐌𝐲 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲 : Telegram - https://lnkd.in/d_PjD86B Whatsapp - https://lnkd.in/dvk8prj5 Built for devs who want to crack interviews — not just solve problems.

Reality Check for Aspiring Software Engineers: If you're not proficient in Data Structures and Algorithms (DSA), you're missing out on numerous opportunities with top tech companies. Mastering DSA is not just about cracking interviews; it's about building a solid foundation for problem-solving and efficient coding. Here's a structured path to guide you through mastering DSA: 1. Array & Hashing: → These basics will form the building blocks for more complex topics. → Recommended Problems: Frequency count, Anagram checks, Subarray sums. 2. Two Pointer & Stack: → Perfect for problems involving sequences and order management. → Recommended Problems: Pair sums, Valid parentheses, Largest rectangle in histogram. 3. Two Pointer ->Binary Search, LinkedList, Sliding Window: → Dive deeper into efficient searching with Binary Search, manage data dynamically with Linked Lists, and tackle contiguous data problems with Sliding Windows. → Recommended Problems: Search in a rotated array, Detect cycle in linked list, Longest substring without repeating characters. 4. Trees: → Understand hierarchical data structures with Trees, manage parent-child relationships efficiently. → Recommended Problems: Binary tree traversal, Lowest common ancestor. 5. Tries, Heap, Backtracking: → Level up with Tries for prefix-based searches, → Heaps for priority management, and Backtracking for exploring possibilities. → Recommended Problems: Word search, Priority queues, Sudoku solver. 6. Backtracking ->Graph, 1D & 2D DP: → Graphs are used for networked data, and Dynamic Programming (DP) → Recommended Problems: Shortest path, Knapsack problem, Unique paths in a grid. 7. Bit Manipulation: → Solve problems with efficient, low-level operations. → Recommended Problems: Single number, Subset generation using bits. 8. Intervals, Greedy, Advanced Graph: → Tackle interval problems for range-based data, use Greedy algorithms for locally optimal solutions, and explore advanced graph topics for complex networks. → Recommended Problems: Meeting rooms, Minimum number of platforms, Maximum flow. ▶️ Resources: → Online coding platforms (LeetCode, HackerRank) → Comprehensive courses (Coursera, Udemy) → Books (Cracking the Coding Interview, Introduction to Algorithms) Pro Tip: Consistent practice and understanding the underlying principles are key. Solve diverse problems and learn from each one. Stay determined, keep learning, and soon you'll be acing those coding interviews! Follow me for insights on Leadership, System Design, and Career Growth!

Trust me, DSA is not HARD. If you follow these 6 steps 𝟭. 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗗𝗲𝗽𝘁𝗵 𝗼𝘃𝗲𝗿 𝗕𝗿𝗲𝗮𝗱𝘁𝗵: - Don't solve 500 coding problems aimlessly. Master around 100 core problems deeply instead. - 40 Problems on Array, Strings, LinkedList, Stack & Queue, Binary search, Trees, Graph, Sorting and Searching: https://lnkd.in/djnaPkeD - 40 Problems on Dynamic Programming (DP), Backtracking, Hashing, Heap, Tries, and Greedy Algorithms: https://lnkd.in/dF3h-Khk 𝟮. 𝗖𝗿𝗲𝗮𝘁𝗲 𝗮 𝗹𝗶𝘀𝘁 𝗼𝗳 𝗸𝗲𝘆 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀: - Use resources like "Strivers A2Z DSA Sheet" by Raj Vikramaditya to curate around 100 core problems. - https://lnkd.in/dQMGy9zF (Strivers) 𝟯. 𝗠𝗮𝘀𝘁𝗲𝗿 𝗲𝗮𝗰𝗵 𝗱𝗮𝘁𝗮 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲: - Understand and implement them by hand. Know how they work internally to ace interview questions. - Fundamentals, Intermediate, Advance DSA topics: https://lnkd.in/d4ws9xfr 𝟰. 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝘄𝗶𝘁𝗵 𝗦𝗽𝗮𝗰𝗲𝗱 𝗥𝗲𝗽𝗲𝘁𝗶𝘁𝗶𝗼𝗻: - Revisit problems after 3 days, a week, and 15 days. Break down solutions instead of rote memorization. - 3:7:15 Rule for DSA: https://lnkd.in/dW6a8wcg 𝟱. 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝗿𝗲𝘂𝘀𝗮𝗯𝗹𝗲 𝘁𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 𝗮𝗻𝗱 𝗰𝗼𝗱𝗲 𝗯𝗹𝗼𝗰𝗸𝘀: - Isolate common patterns like Binary Search or Depth First Search for focused practice. - 20 DSA patterns: https://lnkd.in/d9GCezMm - 14 problem solving patterns: https://lnkd.in/daysVFSz - DSA questions patterns: https://lnkd.in/d3rRHTfE 𝟲. 𝗘𝘅𝗽𝗮𝗻𝗱 𝗶𝗻𝘁𝗼 𝗕𝗿𝗲𝗮𝗱𝘁𝗵: - Once you've mastered core problems and techniques, tackle a wider range of questions. Keep it realistic and relevant to interview scenarios. - 16 Important algorithms problems: https://lnkd.in/dfjm8ked - Tips to solve any DSA question by understanding patterns: https://lnkd.in/d9GVyfBY Additional tips Practice on paper: Practice whiteboard interviews to improve your planning and coding skills without relying on an IDE. It’s a practical way to get ready for real interviews. 𝗚𝗲𝘁 𝘁𝗵𝗲 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻 𝗞𝗜𝗧 𝗵𝗲𝗿𝗲:  - https://lnkd.in/dte69Z5N 𝗙𝗼𝗿 𝗺𝗼𝗿𝗲 𝗵𝗲𝗹𝗽 𝗿𝗲𝗹𝗮𝘁𝗲𝗱 𝘁𝗼 𝗝𝗢𝗕𝗦 - 𝗝𝗼𝗶𝗻 𝗺𝘆 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹 𝗖𝗵𝗮𝗻𝗻𝗲𝗹𝘀 - https://lnkd.in/d4Ht9Ggj - https://lnkd.in/dxqEen4X Stay curious, keep learning, and keep sharing!

🧠 We just implemented the "third paradigm" for LLM learning - and the results are promising. Most of us know that leading AI applications like ChatGPT, Claude, and Grok achieve their impressive performance partly through sophisticated system prompts containing detailed reasoning strategies and problem-solving frameworks. Yet most developers and researchers work with basic prompts, missing out on these performance gains. 🚀 Introducing System Prompt Learning (SPL) Building on Andrej Karpathy's vision of a "third paradigm" for LLM learning, SPL enables models to automatically learn and improve problem-solving strategies through experience, rather than relying solely on pre-training or fine-tuning. ⚙️ How it works: 🔍 Automatically classifies incoming problems into 16 types 📚 Builds a persistent database of effective solving strategies 🎯 Selects the most relevant strategies for each new query 📊 Evaluates strategy effectiveness and refines them over time 👁️ Maintains human-readable, inspectable knowledge 📈 Results across mathematical benchmarks: OptILLMBench: 61% → 65% (+4%) MATH-500: 85% → 85.6% (+0.6%) Arena Hard: 29% → 37.6% (+8.6%) AIME24: 23.33% → 30% (+6.67%) After just 500 training queries, our system developed 129 strategies, refined 97 existing ones, and achieved 346 successful problem resolutions. ✨ What makes this approach unique: 🔄 Cumulative learning that improves over time 📖 Transparent, human-readable strategies 🔌 Works with any OpenAI-compatible API 🔗 Can be combined with other optimization techniques ⚡ Operates in both inference and learning modes 📝 Example learned strategy for word problems: 1. Understand: Read carefully, identify unknowns 2. Plan: Define variables, write equations 3. Solve: Step-by-step with units 4. Verify: Check reasonableness This represents early progress toward AI systems that genuinely learn from experience in a transparent, interpretable way - moving beyond static models to adaptive systems that develop expertise through practice. 🛠️ Implementation: SPL is available as an open-source plugin in optillm, our inference optimization proxy. Simple integration by adding "spl-" prefix to your model name. The implications extend beyond current capabilities - imagine domain-specific expertise development, collaborative strategy sharing, and human expert contributions to AI reasoning frameworks. 💭 What are your thoughts on LLMs learning from their own experience? Have you experimented with advanced system prompting in your work? #ArtificialIntelligence #MachineLearning #LLM #OpenSource #TechInnovation #ProblemSolving #AI #Research

🚀Beyond Pretraining: The Power of Post-Training in LLMs_ Large Language Models (LLMs) have transformed AI, but pretraining alone isn’t enough. Post-training techniques are now essential for improving reasoning, factual accuracy, and ethical alignment. This research paper provides a deep dive into key post-training methodologies. -Why Post-Training Matters +LLMs can generate misleading or inconsistent outputs. +Post-training refines models to align with human intent and improve reliability. -Three Core Post-Training Strategies -Fine-Tuning – Adapts models for specific domains but risks overfitting. -Reinforcement Learning (RL) – Uses feedback to enhance decision-making. -Test-Time Scaling – Improves reasoning efficiency during inference. -Challenges in LLM Reasoning +Catastrophic forgetting – Models lose pretrained knowledge after fine-tuning. +Reward hacking – RL models may exploit optimization loopholes. +Inference trade-offs – High-quality reasoning can slow down responses. -Emerging Trends & Future Research +Hybrid Fine-Tuning – Combining LoRA, adapters, and retrieval-augmented generation (RAG). +RL-based Optimization – Direct Preference Optimization (DPO) & Group Relative Policy Optimization (GRPO). +Human-AI Alignment – Improving transparency and trust in AI outputs. paper: https://lnkd.in/gFeYNPmz github: https://lnkd.in/gaq9vnYe This study provides a structured taxonomy of post-training methods and a public repository to track the latest advancements. #AI #LLMs #MachineLearning #PostTraining #DeepLearning

I used to think learning meant taking courses, attending training, or reading endless PDFs. Until I realized—my biggest lessons happened in the middle of work. ⮡ The time I underestimated a project timeline and had to rebuild trust with my team. ⮡ The time I nailed a strategy, not because I planned perfectly, but because I adapted fast. ⮡ The time I saw a leader embrace mistakes as learning moments—and watched their team thrive. Every missed deadline, every unexpected challenge, every success—it’s all data. The problem? Most teams aren’t using it. Instead of waiting for a formal “learning session,” what if: ✅ We reflected weekly on what worked (and what didn’t)? ✅ We asked why milestones were hit (or missed)? ✅ We made learning a non-negotiable in team meetings? ⮡ Because when you embed learning into work, you don’t just get smarter—you get faster, sharper, and more innovative. I’m breaking this down with two simple but powerful practices for teams in my latest newsletter. Subscribe here → https://lnkd.in/gjzgUUeu What’s one way to make learning a natural part of daily work? #ContinuousLearning #WorkplaceCulture #Leadership #Innovation #TeamDevelopment #OurHappinessMatters #HappinessHabits #HappinessAtWork

RISK isn’t a villain in the market. It is a blind spot in your operating system. 🧠 Buffett’s line is blunt because it is true: “Risk comes from not knowing what you’re doing.” In companies, not knowing shows up as fuzzy units, lagging indicators, and decisions made on vibes. Fix the knowledge gap, shrink the risk. Here is how operators de-risk in practice: ↳ Know the unit. Is your core unit a seat, job, shipment, subscriber, or cohort. price per unit, gross margin per unit, time per unit. ↳ Make time visible. Map the process, measure cycle time and variance at each step, not just the average. Queues create hidden risk. ↳ Promote leading indicators. Pipeline quality, win rates by segment, first-time-right, on-time-in-full, cash conversion days. If it moves the cash or the customer, track it. ↳ Write triggers, not slogans. “If churn for Cohort A hits 3.5 percent in week 8, then launch save flow B within 24 hours.” Decisions should be codified, not debated weekly. ↳ Shorten feedback loops. Smaller batch sizes, frequent releases, fast postmortems, quick refunds. Speed reduces uncertainty, which reduces risk. ↳ Price learning. Treat experiments as line items. 1️⃣ hypothesis, 2️⃣ time box, 3️⃣ decision rule. Learning is an asset when it compounds. Here’s a simple operating playbook: 1️⃣ Clarify the work ↳ One page that defines the unit, constraints, owner, and success metric. ↳ List the unknowns you must burn down this month. 2️⃣ Instrument the flow ↳ One page of leading indicators with thresholds and triggers. ↳ Daily glance, weekly review, monthly reset. 3️⃣ Decide in small bets ↳ Run tight experiments. Ship the smallest change that proves or disproves. ↳ Keep a running “What we learned” ledger. 💡 When you know your unit, time, and triggers, you stop gambling. You are operating. Do these now: ✅ Write your one-page “unit of value,” including price, margin, and cycle time. ✅ Pick three leading indicators and set explicit thresholds with If-Then triggers. ✅ Schedule a 30-minute weekly review to log decisions and lessons learned. ♻️Repost & follow John Brewton for content that helps. ✅ Do. Fail. Learn. Grow. Win. ✅ Repeat. Forever. ⸻ 📬Subscribe to Operating by John Brewton for deep dives on the history and future of operating companies (🔗in profile).

Two core ideas underpin effective inclusive teaching: 1. Cognitive similarity 2. Instructional sensitivity Let’s dig into both… ↓ IDEA 1 The first idea—cognitive similarity—helps us understand that: → the way people learn is more similar than it is different. Despite its intuitive appeal, the notion that students learn best when taught in a way that is unique to their particular needs or preferences lacks empirical support and can even impede learning. Even the term ‘special’ inadvertently reinforces the misconception that certain students learn in fundamentally different ways. While it's true that students differ in their working memory capacity, sensory precision, and prior knowledge… it’s also true that they all learn through the same core cognitive processes, such as paying attention, perceiving similarities/differences, and retrieving ideas. As outlined in 'The Simple Model of Teaching'. NOTES 1. Learn more about this model in Goodrich's book 'Responsive Teaching' 2. This model is adapted from the now famous original by Oli Caviglioli The main implication of cognitive similarity is that: the best thing we can do—for all our students—is to get clarity on this shared mental architecture and then direct the majority of our efforts towards catalysing it via our teaching. This is why approaches such as explicit instruction, high-structure-high-routine environments, and retrieval practice have such robust empirical support... they align strongly with how learning works. IDEA 2 The second core idea—instructional sensitivity—helps us understand that: → students with special educational needs are disproportionately impacted by the quality of our teaching. By definition, disadvantaged learners encounter greater challenges within educational systems, which magnifies the effects of both good—and bad—practice... they are more *sensitive* to instructional quality. This is why practices such as explicit instruction (which reduces cognitive overload), high-structure-high-routine environments (which minimise sensory overload), and retrieval practice (which supports memory) are THE MOST POWERFUL TOOLS for closing the disadvantage gap. If we are serious about making inclusive teaching work, then we’ve got to put cognitive similarity, instructional sensitivity, and their various implications at the heart of our approach. Thanks to big brain Dr Jen Barker for co-developing these ideas. 🎓 For more, check out this paper ⤵️ https://lnkd.in/eeeWGhBi And if you have experiences of effective inclusive practices, please do share them in this brand new repository: → https://lnkd.in/eqpCk28p SUMMARY • Despite our instincts, the way people learn is way more similar than it is different • Our best bet for inclusive teaching is to direct our efforts at catalysing core learning processes • Disadvantaged students tend to feel the effects of this more than others 👊

🌱 “𝐈 𝐝𝐨𝐧’𝐭 𝐟𝐨𝐫𝐜𝐞 𝐭𝐡𝐞𝐦 𝐭𝐨 𝐠𝐫𝐨𝐰. 𝐈 𝐫𝐞𝐦𝐨𝐯𝐞 𝐰𝐡𝐚𝐭 𝐬𝐭𝐨𝐩𝐬 𝐭𝐡𝐞𝐦.” This line hit me hard—because that’s what great teaching truly is. I once had a student who struggled not with ability, but with fear—fear of making mistakes, of raising their hand, of being wrong. Traditional instruction kept nudging them to “speak up more.” But what actually worked? Giving them a safe space to think quietly, letting them submit reflections anonymously, then slowly offering low-stakes speaking opportunities. They bloomed—on their own terms. 🔍 This is what barrier-free learning looks like. Not pushing students harder, but asking: What’s in their way—and how do I remove it? Some powerful methodologies that support this mindset: ✅ Inquiry-Based Learning – Let curiosity drive the lesson. ✅ Scaffolded Instruction – Support step-by-step until confidence builds. ✅ Metacognitive Reflection – Teach students to know how they learn. ✅ Growth-Oriented Assessment – Focus on progress, not just performance. 🌿 Students don’t need force. They need conditions to thrive. #LearnerCentered #Pedagogy #InquiryBasedLearning #GrowthMindset #TeachingStrategies #HolisticEducation #Scaffolding #ReflectivePractice #BarrierFreeLearning