Why a reliable pipeline is essential for ML

🚀 Excited to share my latest end-to-end Machine Learning project: Customer Churn Prediction System 📊 This project goes beyond model training — I focused on building a production-ready pipeline that demonstrates the complete ML lifecycle: ✅ Data preprocessing, feature engineering, and EDA ✅ Model training with hyperparameter tuning (Random Forests) ✅ Deployment as an interactive Streamlit app ✅ Containerization with Docker for easy portability ✅ Automated linting & testing with GitHub Actions (CI/CD) The goal was not only to predict churn but also to design a scalable, reproducible, and deployable ML solution — bridging the gap between data science and MLOps. 🔗 Check out the repo here: https://lnkd.in/dDvGRXvk Always open to feedback, collaborations, and discussions on improving real-world ML workflows! 🌟 #DataScience #MachineLearning #MLOps #Streamlit #Docker #GitHubActions #ChurnPrediction

🔹 Designing a Scalable Image Intelligence Pipeline 🧠 How do you extract meaningful insights from images at scale — efficiently and accurately? I recently worked on designing an image intelligence pipeline capable of analyzing, classifying, and comparing images through an automated, serverless architecture. The focus was on: ⚙️ Building a lightweight, event-driven backend for image ingestion and processing 🧩 Managing metadata intelligently for traceability and fast lookup 🔁 Ensuring clean retention cycles to keep the system optimized and cost-efficient 🎯 Prioritizing accuracy and consistency over raw speed This experience reinforced a key lesson — scalability isn’t just about handling more data; it’s about designing systems that stay reliable and maintainable as they grow. 💬 Curious to hear from others: what’s your biggest challenge when scaling image or data processing pipelines? #CloudArchitecture #SystemDesign #Serverless #DataEngineering #ScalableSystems #BackendDevelopment #AIInfrastructure #AWS #Grupdev

🚀 Training a model is the easy part. Getting it to work in production — that’s where most projects stumble. Every ML engineer learns this the hard way. ⚙️ Here’s what deployment really teaches you: Clean APIs matter more than high accuracy Versioning and logging (MLflow, DVC) are your best friends Streamlit and FastAPI save time when stakeholders want results now 💬 What I’ve learned: The handoff from “data science” to “software engineering” is where great models die. Bridging that gap is what defines an applied data scientist. 👉 Curious — what’s your favorite stack for taking models live? #MachineLearning #MLOps #DataScience #FastAPI #Deployment #ModelServing

When Caching Saves the Day (and When It Doesn’t) ⚡ Databricks lets you cache() DataFrames in memory. Game changer if: You reuse the same dataset across multiple transformations. You’re iterating on ML feature engineering. But beware: Cache too much → cluster runs out of memory, jobs fail. Cache too little → wasted potential. 💡 Rule of thumb: Cache only what you’ll reuse at least twice. ❔ Question: Have you ever had a cache slow you down instead of speeding you up? 👉Read my full article on medium https://lnkd.in/e6asiDBP #Databricks #Spark #Caching

Exploring MLflow Experiments in Databricks 🚀 Today, I ran my first complete MLflow experiment in Databricks — and it really helped me understand how the end-to-end model lifecycle works in practice. Here’s what I observed 👇 🔹 Step 1: I trained a machine learning model using my dataset while MLflow automatically tracked parameters, metrics, and artifacts. 🔹 Step 2: After achieving the desired accuracy, I registered the best model in the Databricks Model Registry. 🔹 Step 3: The registered model can then be deployed in Databricks Apps (or through endpoints) to answer real-time user data queries. It’s fascinating to see how Databricks and MLflow work together — from experiment tracking → model registration → real-time inference — all within one platform. Every experiment teaches something new, and this one made me appreciate how seamless the MLOps workflow can be in Databricks. 💡 #Databricks #MLflow #MLOps #MachineLearning #DataEngineering #DataScience #LearningJourney

🧠 “𝗜 𝗿𝗲𝘃𝗲𝗿𝘀𝗲-𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗲𝗱 𝗚𝗿𝗮𝗽𝗵𝗶𝘁𝗲 — 𝘁𝗵𝗲𝗻 𝗯𝘂𝗶𝗹𝘁 𝗮 𝗽𝗹𝗮𝗻 𝘁𝗼 𝘀𝗰𝗮𝗹𝗲 𝘁𝗵𝗲𝗺.” Ever wondered how a tool like Graphite handles thousands of pull requests and still keeps main always green? I got obsessed with that question. So I did what any engineer with too much curiosity would do — I mapped out the entire system design and built a scaling plan for it on AWS. 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝗜 𝗰𝗮𝗺𝗲 𝘂𝗽 𝘄𝗶𝘁𝗵 👇 ⚙️ 𝙃𝙤𝙬 𝙞𝙩 𝙬𝙤𝙧𝙠𝙨 (𝙢𝙮 𝙩𝙖𝙠𝙚)  • GitHub webhooks → Ingest API → Orchestrator on EKS  • PRs form a DAG → merge queue → AI review engine (Claude via Bedrock)  • Targeted CI/CD → merge → “always green” branch ☁️ 𝙃𝙤𝙬 𝙞𝙩 𝙨𝙘𝙖𝙡𝙚𝙨  • SQS decouples spikes  • EKS + Karpenter auto-scale workers based on queue depth  • Aurora Serverless + Redis + pgvector manage metadata, locks, and embeddings  • Spot CI/CD runners cut cost  • CloudWatch + Prometheus + Grafana keep everything observable 💡 𝙍𝙚𝙨𝙪𝙡𝙩: 𝘼 𝙘𝙤𝙨𝙩-𝙚𝙛𝙛𝙞𝙘𝙞𝙚𝙣𝙩, 𝙨𝙚𝙡𝙛-𝙝𝙚𝙖𝙡𝙞𝙣𝙜, 𝘼𝙄-𝙖𝙨𝙨𝙞𝙨𝙩𝙚𝙙 𝙘𝙤𝙣𝙫𝙚𝙮𝙤𝙧 𝙗𝙚𝙡𝙩 𝙛𝙤𝙧 𝙋𝙍𝙨 — 𝙗𝙪𝙞𝙡𝙩 𝙩𝙤 𝙝𝙖𝙣𝙙𝙡𝙚 𝙗𝙪𝙧𝙨𝙩𝙨 𝙬𝙞𝙩𝙝𝙤𝙪𝙩 𝙗𝙧𝙚𝙖𝙠𝙞𝙣𝙜 𝙢𝙖𝙞𝙣. This isn’t Graphite’s real infra — it’s my interpretation. But it shows how AI + infra + dev tools can make engineering 10× faster and safer. If you love designing scalable systems, I’d love to swap notes — this stuff keeps me up at night. 𝗕𝗶𝗴 𝘀𝗵𝗼𝘂𝘁-𝗼𝘂𝘁 𝘁𝗼 Merrill Lutsky, Greg Foster 𝗮𝘁 𝗚𝗿𝗮𝗽𝗵𝗶𝘁𝗲 📄 𝙁𝙪𝙡𝙡 𝙗𝙧𝙚𝙖𝙠𝙙𝙤𝙬𝙣: “𝘼 𝙉𝙚𝙬 𝘿𝙞𝙧𝙚𝙘𝙩𝙞𝙤𝙣 – 𝙈𝙮 𝙐𝙣𝙙𝙚𝙧𝙨𝙩𝙖𝙣𝙙𝙞𝙣𝙜 𝙤𝙛 𝙂𝙧𝙖𝙥𝙝𝙞𝙩𝙚” #SystemDesign #Graphite #AWS #Infra #AI #DeveloperTools #Scalability #EngineeringDesign #CloudArchitecture

We just announced the OpenAI models launched on Databricks… but Databricks on OpenAI’s data?! Super cool video from OpenAI themselves explaining how they leverage data internally (using Databricks!) to help business and finance teams! https://lnkd.in/g5qY9raV

Implementing Feature Stores for Real-Time ML When I first started working on feature stores, I thought it would be just another data layer-extract features, store them, and use them for model training and inference. But it turned out to be much more than that. Here’s how I approached it Step 1: Standardizing feature definitions The first step was defining what a “feature” really means across multiple pipelines. We created clear metadata, ownership, and transformation logic to avoid duplicates or mismatched definitions across models. Step 2: Ensuring feature consistency Training and serving data had to be identical otherwise, model drift would creep in. To fix that, I used batch & streaming pipelines writing to the same store, ensuring point-in-time accuracy for real-time predictions. Step 3: Optimizing for latency and scalability We used a combination of low-latency storage (like DynamoDB / Redis style lookup) and orchestration pipelines for periodic batch refreshes. This allowed serving millions of lookups with minimal lag. Step 4: Versioning and monitoring Each feature was version-controlled with lineage, so we could reproduce results and roll back easily if needed. We also integrated monitoring and data validation checks to flag anomalies before they reached production. Takeaway: Feature stores aren’t just about storing features they establish a shared foundation between data and ML teams, where consistency, trust, and reusability drive real-time AI success. #DataEngineering #MLOps #FeatureStore #MachineLearning #DataPipelines #RealTimeData

Databricks, Replit, Lovable, Cursor, Claude and more have all launched tools that generate code directly from specs. Natural language instructions directly impact architectural choices, infrastructure decisions, cost and technical risk. PRDs fed to AI agents cascade into expensive infrastructure decisions. Poorly scoped features cost 3-5x more due to agent iteration cycles, with missing edge cases elevating execution risk exponentially. An emerging solution is "Dual-signed specs"—shared PM/Engineering spec docs where both teams collaborate on unified requirements with business context AND technical constraints before agents execute. Dual-Signed specs are: Specific → if a requirement can be interpreted three ways, it will be, and, Standardized → documents with “approved patterns” by default, not tribal knowledge. They include: Structured requirement templates (such as EARS), concrete examples of payloads and interfaces, evals and guardrails. Key metrics: first-pass acceptance rate, cost-per-ticket etc. Bottom-line, the line between "what we build" and "how we build it" is blurred. Product can no longer hand off requirements and walk away. More in comments.

Fine-tuned (PEFT/LoRA) models can greatly outperform out-of-the-box LLMs, but serving them robustly can be challenging due to both numerical issues and performance issues. Our engineering and research teams at Databricks have done amazing work to get up to 2x higher throughput *and* 2-3% better accuracy than open source serving engines for LoRA. Here are some of the techniques we developed: