The Hidden Cost of ML Success: Google Paper Reveals Technical Debt

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

𝐖𝐡𝐞𝐧 𝐈 𝐟𝐢𝐫𝐬𝐭 𝐰𝐨𝐫𝐤𝐞𝐝 𝐰𝐢𝐭𝐡 𝐝𝐚𝐭𝐚 𝐬𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭𝐬, 𝐈 𝐞𝐱𝐩𝐞𝐜𝐭𝐞𝐝… 𝐦𝐚𝐭𝐡, 𝐜𝐨𝐝𝐞, 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬. 𝐖𝐡𝐚𝐭 𝐈 𝐝𝐢𝐝𝐧’𝐭 𝐞𝐱𝐩𝐞𝐜𝐭 👇 ✨ 𝐒𝐭𝐨𝐫𝐲𝐭𝐞𝐥𝐥𝐢𝐧𝐠. Turning numbers into insights people can actually use. ✨ 𝐓𝐡𝐞 𝐩𝐨𝐰𝐞𝐫 𝐨𝐟 𝐚𝐬𝐤𝐢𝐧𝐠 “𝐰𝐡𝐲.” My favorite moment? A client saying: “Glad you asked — I never thought of it.” Most come with a fixed idea and just want a do-it-man. The real value starts with the right questions. ✨ 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧. Magic happens when business and tech work side by side, not in silos. That’s when I realized: AI projects don’t succeed because of algorithms alone. They succeed when t𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐞𝐱𝐩𝐞𝐫𝐭𝐢𝐬𝐞 𝐦𝐞𝐞𝐭𝐬 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐭𝐡𝐢𝐧𝐤𝐢𝐧𝐠. 👉 If you’ve worked with data scientists — what surprised you the most?

The ML Paradox: 90% of Models Never Make It to Production Your data science team just built a brilliant new model. It’s accurate, insightful, and ready to transform your business. But a study by Algorithmia found that up to 90% of machine learning models fail to move from a lab environment to a production application. Here’s why great models often get stuck in the last mile: ●Manual Deployments: The gap between the data science notebook and a scalable, production-ready system is too big. ●Model Drift: Without a proper MLOps pipeline, models lose accuracy and value over time. ●Lack of Monitoring: There’s no system to track a model’s performance or detect when it needs retraining. ●Siloed Teams: Data scientists build in a vacuum, without the engineering support needed to deploy and maintain at scale. The problem isn't the model. It's the pipeline. Nifty Coders bridges this gap by applying DevOps principles to machine learning. We help you build automated, repeatable, and scalable MLOps pipelines that ensure your models are secure, reliable, and continuously delivering value. It's time to stop treating AI as a science project and start engineering it for success. #MLOps #MachineLearning #AIinProduction #DataScience #DevOps #SoftwareEngineering #AI #ScalingAI

Often it’s not the model that breaks — it’s the system around it. Reproducibility, versioning, monitoring, and rollback paths are what make ML a product, not just an experiment. This is where MLOps proves its real value. #MLOps #AI #MachineLearning #DataScience #Engineering #AIProduction

🚀 Breaking into machine learning isn’t just about knowing the latest models. It’s about avoiding the rookie mistakes that can hold you back. From mentoring engineers and starting out self-taught myself, I’ve noticed 5 red flags that instantly reveal inexperience: 1️⃣ Focusing only on the model – A great model in a notebook means little if it can’t be deployed. Think end-to-end system design. 2️⃣ Starting too complex – Don’t jump to deep learning right away. A simple SQL query or linear regression might solve 80% of the problem. 3️⃣ Weak software engineering – ML is still software. Without CI/CD, modular code, and manageable PRs, projects quickly fall apart. 4️⃣ Misusing EDA – Plots for the sake of plots ≠ insight. Purposeful exploration should guide feature engineering and model choice. 5️⃣ Misunderstanding metrics – Accuracy alone won’t cut it. Tie your metrics back to business outcomes and watch out for pitfalls like class imbalance or data leakage. The good news? Every one of these mistakes is fixable with practice and the right mindset: ✔️ Start simple, then iterate. ✔️ Build with deployment in mind. ✔️ Treat ML as software engineering. ✔️ Always connect metrics to real-world impact. 👉 Which of these have you seen most often in real projects? #MachineLearning #CareerGrowth #DataScience #EngineeringLeadership #AI

🚀 𝗧𝗿𝗮𝗰𝗸𝗶𝗻𝗴 & 𝗠𝗮𝗻𝗮𝗴𝗶𝗻𝗴 𝗠𝗟 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝘀 𝘄𝗶𝘁𝗵 𝗠𝗟𝗙𝗹𝗼𝘄 After automating MLOps pipelines with GitHub Actions, the next critical step is tracking, managing, and reproducing experiments — that’s where MLflow comes in! 🔑 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗠𝗟𝗙𝗹𝗼𝘄? MLflow is an open-source platform that helps data scientists and ML engineers track experiments, store models, and deploy solutions efficiently. ⚙️ 𝗞𝗲𝘆 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: 1️⃣ Experiment Tracking → Log metrics, parameters, and results for every run 2️⃣ Model Registry → Version models, stage them, and manage lifecycle 3️⃣ Projects → Package code and dependencies for reproducibility 4️⃣ Model Serving → Deploy models easily via APIs or cloud platforms 💡 𝗪𝗵𝘆 𝗠𝗟𝗙𝗹𝗼𝘄 𝗶𝘀 𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁: ✅ Helps teams compare models and pick the best performing one ✅ Ensures reproducibility across different environments ✅ Integrates with popular ML libraries like TensorFlow, PyTorch, and Scikit-learn ✅ Bridges the gap between research and production 🌐 𝗪𝗵𝗲𝗿𝗲 𝘆𝗼𝘂 𝗰𝗮𝗻 𝘂𝘀𝗲 𝗶𝘁: ✔ AI-powered products like recommendation systems, fraud detection ✔ Healthcare diagnosis models, predictive maintenance, etc. ✔ Any ML project where experiment tracking and version control matter 🖼 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗠𝗟𝗙𝗹𝗼𝘄 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 Here’s how MLflow works in real-world environments! The diagram below shows a typical setup where the MLflow Tracking Server integrates with PostgreSQL for metadata and Amazon S3 for storing artifacts and models — enabling scalable, collaborative experiment tracking. 📢 𝗦𝗵𝗮𝗿𝗲 𝘆𝗼𝘂𝗿 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲! If you’ve tried MLflow or are planning to, share your experience below — What’s the biggest challenge you face while tracking experiments? #MLflow #MachineLearning #MLOps #AI #DataScience #ModelTracking

We just launched a new course on Coursera that we developed jointly with famed Stanford professor Andrew Ng and his team at DeepLearning.AI. This course is a great way for you to get hands on experience with AI and even use AI to assist in generating code. Course: Fast Prototyping of GenAI Apps with Streamlit and Snowflake). The course is taught by "The Data Professor" Chanin Nantasenamat, with a lecture from Professor Andrew Ng. You can find all our courses on Coursera, including an Intro to Snowflake and courses in Data Engineering and Gen AI. You will earn certificates that you can publish on your LinkedIn profile. Link below.

Thought Snowflake was just a data warehouse? If so, you're living in the stone age. ❄️ Snowflake and DeepLearning.AI just released a Coursera on 'Fast Prototyping of #GenAI Apps with Streamlit' (link below) #Snowflake #AI #Data #DeepLearning #Coursera https://lnkd.in/gYDVxBer

Most AI projects never make it past the Jupyter notebook. The code works in isolation, the demo looks clean, the metrics sparkle and then everything stalls. I’ve lived through that transition, and it’s where the real work begins. Because moving from a notebook to a production API is less about model accuracy and more about system design. It’s where experiments become products. And it’s where technical choices start to carry financial consequences. The lessons I’ve learned are simple but brutal: • A model without data pipelines is just a prototype. • A service without observability becomes a black box. • An API without ownership drifts until no one trusts it. The fastest way I’ve found to operationalize is to treat the notebook as a sketch, not a blueprint. The blueprint comes when you ask: • How will this scale when data triples? • Where will feedback live? • Who owns the decision boundary? Every founder who wants AI in production needs this mindset. Because value doesn’t come from accuracy at 82%. It comes when that 82% turns into an API your ops team can trust daily. That’s the truth. P.S. What’s been your hardest leap from experiment to product, or from product to scale?

Over the past 3 years, I’ve focused on data engineering: 🏗️ Building highly optimal and scalable pipelines ✅ Ensuring data quality 📈 Enabling analytics that drive business decisions It has been a journey of solving tough technical problems and learning to see data not just as tables and schemas but as the foundation of decision-making. ✨ Now, I’m extending this journey into the world of AI Engineering and Large Language Models (LLMs), where I am exploring embeddings, tokenization, attention, fine-tuning, deploying, and applying them to real-world problems. Why? Because: 🔋 Clean, well-structured data is the fuel that powers today’s AI models. 📚 Concepts like RAG (Retrieval-Augmented Generation) rely heavily on the pipelines and governance that data engineers already excel at. 🧠 The future isn’t just about moving data, it’s about enabling systems that can reason with it. What excites me most is that my background gives me a strong foundation for this next chapter. The skills I’ve built in distributed systems, data pipelines, and governance are directly transferable to building, fine-tuning, and deploying AI systems. 👉 This isn’t a pivot away from data engineering, it’s an extension of it. I believe the strongest AI engineers of the future will be those who deeply understand data. Over the next months, I’ll be sharing what I’m learning as I build hands-on projects in LLMs and AI engineering—starting small, documenting the challenges, and celebrating the wins. 🤝 If you’re also on this journey (or curious about starting), let’s connect and learn together.