Harivdan N.’s Post

🚀 Day 14 – My Learning & Sharing Series Continuing the journey of sharing my notes and resources so we can all learn and grow together. 🌱 Today’s share: 1️⃣ Machine Learning – Regression Concepts 🤖 Covering key foundational topics: 🔹 Linear Regression 🔹 Regression Metrics 🔹 Multiple Linear Regression 🔹 Gradient Descent 🔹 Polynomial Regression 🔹 Bias–Variance Tradeoff 🔹 Regularization Regression forms the backbone of many machine learning models — mastering it builds a strong base for advanced techniques. 📚✨ #MachineLearning #Regression #DataScience #LearningResources #ContinuousLearning #CareerGrowth

MLflow vs Comet vs Weights & Biases – Which MLOps Tool Should You Pick? Choosing the right experiment tracking & model management tool can make or break your ML workflow. Here’s a quick comparison: 🔹 MLflow ✅ Open-source & widely adopted ✅ Great for model tracking, registry & deployment ✅ Easy integration with Python-based ML pipelines ⚠️ UI is basic compared to others 🔹 Comet ✅ Rich experiment tracking & visualization ✅ Hyperparameter optimization & dataset versioning ✅ Collaboration-friendly dashboards ⚠️ Limited free-tier compared to MLflow 🔹 Weights & Biases (W&B) ✅ Powerful real-time logging & visualization ✅ Scales beautifully for large teams & projects ✅ Strong support for deep learning workflows ⚠️ Can feel heavy for small/simple projects 💡 Takeaway: Use MLflow if you want full control & open-source flexibility Use Comet if you want an intuitive, experiment-focused platform Use W&B if you need enterprise-level ML observability & collaboration Which one are YOU using for your MLOps stack? Comment below! 👇 #MLOps #MLflow #CometML #debadipb #profitsolutions #WeightsAndBiases #AI #MachineLearning #ModelOps #AIInfrastructure #DataScience #MLTools #AICommunity #ExperimentTracking

Chunking Techniques for Retrieval-Intensive Applications 🧠  If you’re building RAG systems, how you chunk your data can make or break performance. Here’s a quick breakdown of the 6 most popular techniques: #RAG #GenerativeAI #AIEngineering #LLMOps #VectorDatabases #AIForDevelopers

🗓 Day 34 of my #BackToFlow journey to rebuild consistency — back to Machine Learning basics with Simple Linear Regression 📈 Today’s focus: Introduction to Simple Linear Regression → One of the simplest yet most powerful ML algorithms that models the relationship between two variables. Understanding the Equation → Form: y = mx + c Where m = slope (how much y changes with x), and c = intercept (where the line crosses y-axis). Explored how this line is used to predict outcomes and minimize error between predicted and actual values. Even though it’s a beginner-friendly algorithm, it’s the foundation for more complex regression and ML models. 🚀 #MachineLearning #LinearRegression #DataScience #LearningJourney #BackToFlow #Consistency

🚀 MLOps in the Wild #1 We've all been there: models sitting as pickles in S3 buckets, APIs hand-rolled for each new version, and deployment pipelines that feel more like digital archaeology than modern engineering. The reality? Many organizations are still treating ML models like one-off science experiments rather than production systems that need to scale, monitor, and evolve. This is where MLOps truly shines – not as another buzzword or complex framework to learn, but as a way to make machine learning more transparent, reliable, and accessible for everyone on the team. When data scientists can deploy with confidence, when engineers can monitor model performance in real-time, and when business stakeholders can actually understand what's happening under the hood – that's when ML starts delivering real value. 💭 What's your biggest challenge in making ML work in production? Is it the tooling, the processes, or something else entirely? #MLOps #MachineLearning #DataScience #ProductionML

🚀 New Series Alert: Algorithms in GenZ 🚀 Ever felt like algorithms sound too complicated, full of math-y jargon that makes your brain want to Ctrl+Alt+Del? Well… not anymore! I’m starting a weekly series called “Algorithms in GenZ” where I break down data science + machine learning algorithms in the most relatable way possible using memes, everyday analogies, and some GenZ lingo. Think: Decision Trees 🌳 → your messy breakup choices K-Means 🤝 → friend groups forming based on vibes PCA ✨ → Marie Kondo but for datasets The goal? Make algorithms less scary, more fun, and 100% learnable for anyone who scrolls. 👉 First article drops next week on Medium. Stay tuned - and if you’ve ever wanted algorithms explained without the headache, this series is for you. #Algorithms #DataScience #MachineLearning #GenZ #SyntheticData #Medium

The more I read about ML, the more I see parallels with problems we’ve been tackling in systems engineering for years. • Latency → in ML it’s inference time. • Reliability → in ML it’s handling noisy or missing data. • Scaling → in ML it’s training on massive datasets instead of traffic. The math is new to me, but the mindset isn’t: designing for real-world constraints. That makes this leap into ML feel less like starting from scratch, and more like extending the same engineering playbook to a new domain. #MachineLearning #AppliedAI #SystemsEngineering

Why You Only Learn ML by Living Through the Full Cycle (Again and Again) Many people think learning ML is about books, tutorials, or a few quick models. But the real learning happens when you go through the entire lifecycle repeatedly. Here’s what it looks like in practice ✅ Pick a real dataset. Something meaningful enough to reflect real-world messiness. ✅ Train and deploy a model. Get it running and connect it to a simple dashboard (for example, in Streamlit). ✅ Check the results. The dashboard will likely fall short of expectations. At this stage, there are two options: 👉 Stop out of frustration. 👉 Or pause and ask: “What went wrong? Data quality? Pipeline design? Model choice? Evaluation setup?” This reflection leads straight back into the ML lifecycle: ⟶ Rethink the problem ⟶ Adjust and clean the data ⟶ Tune or redesign the model ⟶ Re-deploy and test again And this loop fail, adjust, repeat is what truly builds ML engineers. The truth is you don’t master ML by avoiding failure. You master it by moving through the cycle again and again until it becomes second nature. #machinelearning #data #problemsolving ##ArtificialIntelligence

🌾 Sowing the seeds of responsible AI adoption with Defra. Earlier this year we collaborated with the Department for Environment, Food and Rural Affairs (Defra) to explore how GenAI could cultivate smarter software delivery. Instead of chasing quick wins, we focused on preparing the ground for long-term growth; disciplined engineering, structured workflows, and governance-first AI integration. At the heart of this? The Defra Playbook 🌱 (a practical guide for integrating AI into software engineering workflows). Early harvest from the pilot included: ✅ Engineers reporting up to 5x faster prototyping ✅ Higher code quality through AI + human oversight ✅ Consistent outputs via a standardised prompt library Our case study details how structured AI adoption can help organisations grow stronger roots for scalable change. Read the case study here: https://lnkd.in/ePr7xQEK #CaseStudy #PublicSector #ResponsibleAI #DataAndAI

Big ML projects get all the spotlight. But in reality, some of the biggest ROI comes from smaller, focused use cases: Automating a repetitive step Personalizing a simple recommendation Catching small errors early Simple doesn’t mean trivial — it means practical. What’s one small ML use case you’ve seen that paid off big? #DataScienceInAction #MachineLearning #SmartAutomation