Kubeflow Trainer integrated into PyTorch Ecosystem

🚀 Day 1 of My #LLMOps Journey: What the Heck is LLMops Anyway ? So… I’ve been diving into this whole LLM (Large Language Model) world, and one term keeps popping up: "LLMops". At first, I was like, “Wait… isn’t this just MLOps but fancier?” 🤔 Turns out, it’s not exactly the same. LLMops is all about "managing, deploying, and monitoring" large language models in the real world. And trust me, these aren’t your usual ML models – we’re talking billions of parameters, crazy compute costs, and outputs that can surprise you in ways you’d never expect. Here’s what I learned today: 1. LLMops = Think "MLOps 2.0", but for huge language models. 2. It’s not just training – it’s deployment, scaling, safety, and continuous improvement. 3. If done right, LLMops lets you build practical AI systems like chatbots, task-automation agents, and even multi-agent workflows. I’m starting this series to learn LLMops in public. Every day, I’ll share my experiments, struggles, and little wins so that: You can see what it takes to actually deploy and manage LLMs, And I can keep myself accountable (because trust me, I’ll forget things if I don’t write them down ). 💡Have you ever tried deploying an LLM or even a smaller AI model? What was your biggest struggle? Drop a comment – I’d love to hear your experience! #LLMOps #AppliedAI #MachineLearning #LearningInPublic #GenerativeAI

AI + Helm in Kubernetes is opening up a new way of working! Tools like ChartSmith.ai are showing how natural language prompts can streamline Helm chart authoring, validation, and deployment. Instead of manually writing complex YAML, you can now:  Prompt – start with plain language  Plan – AI interprets intent  Generate – Helm chart auto-created  Eval – validate against best practices  Review – quick human feedback loop  Accept – ready-to-deploy, reliable Helm chart This hands-on experience showed me how AI can: Simplify Helm chart creation for any team Reduce errors with automated checks Speed up Kubernetes workflows in CI/CD and GitOps pipelines Excited to see how AI copilots like this evolve and make Kubernetes more accessible, efficient, and reliable for everyone! #Helm #Kubernetes #AI #DevOps #CloudNative

I spent 16 days building my first LangGraph AI agent. Here's what most tutorials won't tell you about AI workflows: For the past 16 days, I've been waking up at 5 AM to learn LangGraph—before work, before my kids wake up. 1.5 hours daily, zero prior experience, one goal: understand stateful AI workflows, not just copy-paste code. The "Aha!" Moment (Day 3): I was building conditional logic—"should my agent ask questions or generate a proposal?" In traditional code: nested if-statements everywhere. In LangGraph: one explicit conditional edge. I could SEE the decision point. The flow was visual, traceable, simple. That's when it clicked: LangGraph gives you a mental model you can hold in your head. What I Built: A 6-node proposal generator that: Extracts requirements from vague text Validates completeness Flags unrealistic expectations Calculates effort and costs Generates professional proposals The Hardest Part: Not LangGraph (learnable in a week). Building good TOOLS—like the cost calculator. Spent Days 10-14 improving estimation logic. The framework is straightforward. The domain expertise is where it gets hard. Results: 6 nodes, 15 tests, 100% pass rate. Handles vague inputs and edge cases , 24 hours invested, complete understanding. Fully open sourced Key Lessons: State-first thinking is powerful—forces clarity upfront Nodes are just functions: state in → process → state out Adding features is trivially easy—just add a node Perfect is the enemy of learning What's Next: Agent #1 of 6 in my path to AI Solutions Architect. Each teaches new concepts: → Agent #2: Evals & QA layers → Agent #3: Memory & conversations → Agent #4: Multi-agent collaboration For anyone learning LangGraph: Stop watching tutorials. Build something messy. Make it work. Make it better. That's real learning. Open sourced everything: https://lnkd.in/gkv9zRzd Full breakdown on Medium https://lnkd.in/gYtVUPde If you're building agents or learning LangGraph—let's connect. I'd love to hear what you're working on. #AI #LangGraph #MachineLearning #AIAgents #LearningInPublic #OpenSource

While #OpenAi has been making all the noise, Google Research quietly published this intellectually stimulating paper that breaks down the inner workings of in-context learning … and how #LLMs can adapt to examples in a prompt without ever changing their stored weights. When given a few examples (via prompts), the model doesn’t rewrite its weights. Instead, it performs a temporary rank-1 adjustment, a small, low-cost internal tweak that captures the pattern for that moment. Once the prompt or data stream ends, the tweak disappears, leaving the core model unchanged. While LLMs still have their fundamental architecture limitations and challenges, I am intrigued by this type of adaptive learning that could have strong implications for applied AI in the industrial context. It means future AI systems could potentially: • Adapt in real time to dynamic factory data or process changes • Optimize energy use or quality yield on the fly • Continuously learn from operator feedback and then reset safely • Deliver personalization and insight without long retraining cycles The holy grail of “ephemeral learning” that could redefine adaptive intelligence in industrial environments where models evolve continuously with context, and don’t require additional code. The ability to automate while being able to self-tune, on the fly can be truly game changing. While this paper is a significant step forward in explaining how in-context learning might work, there have been other explanations like Bayesian conditioning, suggesting there are multiple routes to this. Despite its simplified assumptions, for now, this gives us a useful peek into a future that could someday adapt systems on-the-fly without retraining. Raghunandan Mathur Elton Sampaio Vijay Raghavan Ph.D., M.B.A., Sayan Maity, PhD Amy Daali, PhD John Pasmore Ariana Smetana Dr. Kruti Lehenbauer

🧠 From Language to Learning: The Practical Roadmap for the Next AI Wave For a decade we taught machines to understand. Now we must teach them to act—and that shift changes everything. Reinforcement Learning (RL) is not academic theater; it’s the infrastructure for decision-making AI. When you combine modern LLMs (reasoning + memory) with rich RL environments, you get systems that test hypotheses, run experiments, and learn from consequence—at scale and speed. Why environments beat bigger models •Signal over scale. Bigger models generalize; richer environments specialize intelligence by exposing it to consequential tradeoffs. •Transfer through fidelity. Well-designed gyms create out-of-distribution robustness: the environment exposes edge cases, adversarial behavior, and rare events before they hit production. •Compound learning. Every iteration improves not only the agent but the environment’s scenario coverage—creating a virtuous feedback loop that multiplies ROI on compute and data. Where to apply RL now (practical filters) Use RL when: 1.Decisions are sequential and impact future state (supply chains, pricing, clinical trials). 2.Feedback is delayed or sparse (long-horizon rewards). 3.Real-world testing is costly or risky—so simulation yields high marginal value. Turing’s lever: RL Gyms as a growth engine Turing’s path to product-market fit and monetization is straightforward: •Gym-as-Service: prebuilt environments for common enterprise domains (logistics, fraud, pricing). •Digital twin engagements: co-train agents with customer data in sandboxed sims to de-risk rollouts. •Licensing & co-development: white-label environments + model co-training for vertical players. This creates three revenue vectors: subscriptions, professional services, outcome-share pilots. Concrete value examples (how customers win) •Faster hypothesis velocity: run thousands of policy variants overnight. •Reduced deployment risk: simulate product changes on a digital twin before launch. •Operational leverage: automate routine strategic decisions and free expert time for high-value work. How to measure success (KPIs to track) •Learning velocity: episodes to baseline performance. •Sim→Real gap: performance delta between simulation and first-month production. •Business lift: % improvement in target metric (throughput, cost, conversion) in pilot deployments. •Time-to-value: days from sandbox to production trial. The near-term market opportunity The market is shifting from “model licensing” to “learning infrastructure” — the teams that own environments and feedback loops own downstream value: models, deployments, and recurring revenue. We’re at the inflection where LLMs provide the cognition, and RL Gyms provide the curriculum. Build the world. Train the agent. Scale the loop. #AI #ReinforcementLearning #LLM #DigitalTwins #Turing #MachineLearning #AIEconomy #ProductStrategy

✨ **Day 13 of my Generative AI Learning Journey** ✨ One big challenge with **Generative AI** is that the output is often **unstructured text**. While this is great for flexibility, it’s not directly usable in ML or analytics pipelines. Today I learned how to **convert GenAI outputs into structured data** by: 🔹 Defining a schema with **Pydantic** 🔹 Forcing the model to return JSON that fits the schema 🔹 Parsing results into a **Pandas DataFrame** for analysis or downstream ML tasks Example: I asked a model to list popular programming languages with their year, creator, and use cases. Instead of messy text, I got a clean, structured dataset ready for ML. 💡 Key takeaway: By enforcing structure at the generation step, we turn unstructured GenAI text into **direct pipeline-ready data**. This bridges the gap between **creative generation** and **real-world data workflows**.

It feels harder than ever to keep up with new technologies these days. The best example, of course, is AI and LLMs. Last August, OpenAI released GPT-5, which in my experience does a better job of “thinking” while not patronizing me with the usual “great point!” or “perfect, you’re so smart” replies. Then at the end of September came Claude 4.5 Sonnet, and after using it for coding this past week, I can honestly say it’s my favorite so far. It feels on par with GPT-5 in performance but noticeably faster, with a larger context window that makes coding smoother. Now OpenAI is rolling out Sora 2, which improves on the original with better quality, faster rendering, and audio capabilities. I haven’t had the chance to try it yet since it’s still invite-only, but it’s both astounding and a little terrifying to see how fast this technology is advancing. With all these updates flying by, how are we supposed to stay informed? Truthfully, I’m still figuring that out. Personally, I try to spend 30 minutes to an hour each morning catching up on tech news from my YouTube feed or daily.dev, which is great for web development updates. With how quickly things evolve, unless you’ve found your niche in a specific area, I think the best approach is to follow expert discussions in each field. You can always go deep later, but having broad awareness of what’s happening across the landscape feels more practical right now. What about you? How do you keep up with the latest tech trends, and do you have any go-to resources you’d recommend? #AI #Technology #Innovation #SoftwareEngineering #Learning #CareerGrowth #DeveloperCommunity

🧩 Picking up from my last post… AI isn’t just helping us build anymore — it’s building with us. From Cursor writing code that feels eerily familiar, to Lovable spinning up apps in minutes, to GPT evolving faster than frameworks — we’re already living in a world where AI isn’t the assistant, it’s the architect. I wrote about this quiet shift — how AI moved from being a tool to being a teammate. 👉 Read the full piece here: https://lnkd.in/dGtAGRQj #ArtificialIntelligence #MachineLearning #GenerativeAI #AIinTech #AITools #AIDevelopment #FutureOfWork #TechInnovation #DeveloperExperience #EngineeringLeadership #BuildWithAI #CursorAI #LovableAI #GPT5 

I finally understand “Attention Is All You Need” — the paper that changed AI forever After spending time diving deep into the 2017 Transformer paper by Vaswani et al., I can confidently say… it finally makes sense! Here’s how I now see it: Before Transformers Old sequence models like RNNs and LSTMs read data step by step. That meant: They couldn’t process words in parallel They struggled to remember long-range dependencies Training took forever Then came the Transformer : a model that ditched recurrence and convolutions completely, relying only on a mechanism called attention. What “attention” really means: Imagine reading a sentence and focusing on the words that matter most to understand the current one. That’s what the model does — for each word, it computes how much attention to pay to every other word. Mathematically: Attention(Q, K, V) = softmax(QKᵀ / √dₖ) V This lets the model connect distant words directly, no matter how far apart they are. Architecture has two big parts: 1. Encoder – Reads and understands the input sentence. Each encoder layer has: Multi-head self-attention: learns relationships between words in the same sentence. Feed-forward layer: refines those relationships token-wise. Add & Norm: stabilizes training via residual connections + layer normalization. 2. Decoder – Generates the output sentence. Each layer adds: Masked self-attention: predicts words one by one, without “peeking” ahead. Cross-attention: looks at encoder outputs to decide which input words to focus on. Feed-forward layer: transforms the output representation. Each word also gets positional encoding (sine/cosine patterns) so the model knows where each token is in the sequence — since attention alone ignores order. Why it’s revolutionary Parallelization: All tokens processed together → huge training speedup. Global context: Every token can attend to every other token → better understanding. Scalability: Stack more layers and heads → better performance without major redesigns. Universality: The same idea now powers GPT, BERT, T5, and almost every modern LLM. Training insights I found fascinating Uses the Adam optimizer with a special warmup + decay learning rate schedule. Applies label smoothing (ε=0.1) to avoid overconfidence. Shares weights between embedding and softmax layers for efficiency. The “base” model used 6 layers, 8 heads, 512-dim embeddings, and achieved 28.4 BLEU on English→German translation. The Transformer learns relationships between all words in a sentence simultaneously and globally instead of one step at a time. That single idea opened the door to GPTs, BERTs, and every generative AI model we use today. I’m honestly amazed how such an elegant architecture replaced years of complex recurrence research with one idea: "Attention is all you need."

How to design agents for relevant and focussed learnings with less data and more compute? Amazing context set by Edan Meyer in his video (https://lnkd.in/gdXZTDRe) Edan Meyer outlines three key areas of research that the field needs to focus on - areas often avoided in popular discourse - to achieve a good vision of AGI, which he defines as a general-purpose learning algorithm inspired by animal learning. 1️⃣ Continue Learning: This means the agent should never stop learning. While current models utilize fine-tuning (training on a new task after initial training) and in-context learning, these methods have significant caveats. When trying to fine-tune a model multiple times, researchers often encounter 🤔 "catastrophic forgetting" (the model forgetting what it previously knew) or a ☠️ "loss of plasticity" (the model slowly losing its ability to learn over time). Furthermore, information learned via in-context learning is only learned temporarily until that information exits the context window. 2️⃣ The Ability to Learn from a Single Experiential Stream of Data: This requirement is the exact opposite of how current Large Language Models (LLMs) are trained. Current LLMs are typically trained by randomly sampling from billions of disjointed segments of text (like passages from books 📚 , articles 📰, or transcripts 📜 ) and feeding them to the model in enormous batches. If a model only learns from disjointed sequences, it will never gain the ability to reason about how it should act over much longer time horizons. ⏲️ Without experiencing the world through a continual stream of observations, like humans do, concepts such as episodic memory 🤔 and temporally correlated causality 🧐 become meaningless. 3️⃣ Scaling with Compute (Designing algorithms where more compute always leads to better performance): While current methods have figured out how to scale models to hundreds of billions or trillions of parameters when using massive static data sets 🔢 , they have not figured out how to scale effectively in the continual single stream experiential learning setting. Current learning methods fail when training massive models without a massive static data set or when stopping the use of huge batches of data because they were designed with a "big data mindset from the ground up". The goal 🥅 is to develop methods that, when given more compute, can use that compute to extract more out of what they are experiencing moment to moment. 😇 My 2 cents : Hit back the basics of Reinforcement Learning and Deep Learning, we have the answers and few models in last few months are setting the stage where step-wise-breakdown-and-problem-solving is ingrained into the training methodology of model leading to solving his 3rd point.