"Learn Experiment Tracking with MLflow from Azumo"

🤫 The Secret to Multi-Million Dollar AI Models is Revealed! If you think you can build powerful AI models just by relying on fancy algorithms (like Transformers), let me tell you, I was burned by that exact misconception, and my project failed because of it! Data Quality = AI Model Quality In this post, I share my personal journey and lessons learned, covering everything from: Why Internal Data is the most valuable. How to fight Unstructured Data challenges (images, text). The advanced tools (Spark, Airflow) are used for production-scale projects. What challenges did you face in your first data collection projects? What's your favorite tool? 👇 #AI #DataCollection #MachineLearning #BigData #DataScience #DataEngineering #Apache

🚀 𝐄𝐯𝐞𝐫𝐲𝐨𝐧𝐞’𝐬 𝐭𝐚𝐥𝐤𝐢𝐧𝐠 𝐚𝐛𝐨𝐮𝐭 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞. 𝐅𝐞𝐰 𝐫𝐞𝐚𝐥𝐥𝐲 𝐠𝐞𝐭 𝐢𝐭. 𝐃𝐨 𝐲𝐨𝐮? 👇 Most people think it’s just coding and AI. But that’s only the surface. 💡 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 = 𝐭𝐮𝐫𝐧𝐢𝐧𝐠 𝐝𝐚𝐭𝐚 𝐢𝐧𝐭𝐨 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬. It’s not about algorithms, it’s about understanding. Not about data, but about clarity. And no — you don’t need to be a math genius to start. I broke down what Data Science actually means here ⬇️ 🔗 Read the full story on Medium (link in comments). #DataScience #AI #MachineLearning #Analytics #LearningJourney #CareerGrowth

Everyone talks about data scientists and AI researchers. But without data engineers, none of it would actually work. They build the invisible systems — the pipelines, the transformations, the storage layers — that feed every AI model with the right data, at the right time. When your LLM generates an answer, or your dashboard updates in real time… somewhere behind the scenes, a data engineer made that magic possible. In the world of AI, models get the spotlight — but data pipelines make the show run. If you’re learning ML or AI, don’t skip data engineering — it’s where real scalability begins. #DataEngineering #AI #MachineLearning #MLOps #BigData #Analytics

Struggling to Pick the Right AI Algorithm? This Cheat Sheet Breaks It Down by Use Case! From Text Analysis to Image Classification, Anomaly Detection to Recommender Systems — get clarity on what works best, where. Perfect for: AI Engineers, Data Scientists, ML Beginners Save this post — your next project will thank you. To get complete guide for free: 1. Connect with me 2. Like this post 3. Comment “Deep Learning” below, and I’ll send it to you! Pdf credit goes to respective owner. Follow Pratham Chandratre for more!

Day 3: Feature Engineering Journey 🚀 Today, I continued my exploration of Feature Engineering with a focus on Feature Scaling — a vital step in preparing data for machine learning models. 💡 What I explored today: What exactly is Normalization? The effects of Normalization on data and model performance. Different types of Normalization Simple definitions and practical examples When Normalization is essential in Machine Learning? Feature Scaling might seem simple, but it plays a huge role in ensuring that models learn efficiently and perform accurately. 👉 If you’re a Data Scientist or ML enthusiast, what’s one tip you’d give a beginner about Feature Scaling? Drop your thoughts or favorite learning resources in the comments — I’d love to hear from you! 🙌 I’ve also created a note of today’s learning to document my progress. #FeatureEngineering #FeatureScaling #DataScience #MachineLearning #LearningJourney #AI

✍️ Leveling Up in AI: My Databricks AI Agent Fundamentals Journey ✨ I’m excited to share my recent deep dive into AI Agent Fundamentals via Databricks Learning! During the course, I created detailed hand-written notes and captured screenshots, turning complex topics into actionable insights. Here’s a glimpse of what I explored: -What are AI Agents? Understanding how autonomous systems make decisions and act intelligently. -Frameworks & Core Concepts: Dissecting LLMs (Large Language Models), GenAI, and their role in agent frameworks. -Prompt Engineering: Mastering the art of designing effective prompts for AI behavior. -Practical Implementation: Building intelligent applications in the Mosaic AI Playground, getting hands-on with Agent Bricks. -General Intelligence vs Data Intelligence: Comparing broad intelligence with data-driven decision-making. -AI Application Challenges: Insights on scalability, reliability, and ethical considerations. -Agentic vs Non-Agentic Systems: Understanding architectures and their real-world impact. I've also attached a PDF of my notes and key screenshots to help others who are embarking on this learning path. Exploring these concepts has sharpened my ability to design, build, and deploy advanced AI solutions! 🔗 If you’re interested in learning more, you can check out the course here: https://shorturl.at/ZS9ms PS: All screenshots and handwritten notes are shared solely for learning purposes—no company information has been breached or disclosed. #AI #AIAgents #Databricks #DeepLearning #LLM #MosaicAI #PromptEngineering #HandwrittenNotes #LearningJourney #Innovation

Day 2: Feature Engineering Journey 🚀 Today, I continued my exploration of Feature Engineering with a focus on Feature Scaling — a vital step in preparing data for machine learning models. 💡 What I explored today: Understanding Feature Scaling and its importance. The concept of Standardization, a key type of Feature Scaling. The effects of Standardization on data and model performance. When Standardization is essential in Machine Learning. Feature Scaling might seem simple, but it plays a huge role in ensuring that models learn efficiently and perform accurately. 👉 If you’re a Data Scientist or ML enthusiast, what’s one tip you’d give a beginner about Feature Scaling? Drop your thoughts or favorite learning resources in the comments — I’d love to hear from you! 🙌 I’ve also created a note of today’s learning to document my progress. #FeatureEngineering #FeatureScaling #DataScience #MachineLearning #LearningJourney #AI

Uber Eats tackles position bias with a cutting-edge deep learning approach. Their research team recently unveiled a novel method to mitigate position bias, where users tend to favor higher-ranked stores regardless of relevance. By refining their model architecture on biased interaction data, Uber Eats effectively debiases the conversion rate to reveal true conversion probabilities. Their innovative solution involves a deep learning CVR model with a dedicated position bias side tower, enabling simultaneous estimation of True CVR and Position Bias. Careful feature selection and regularization ensure each tower operates independently, enhancing home feed recommendations and boosting user orders. Dive into my detailed video exploring these biases in recommender systems and Uber Eats' groundbreaking approach. Video Link: youtu.be/ZCO75OuMRY0   Channel Link: youtube.com/@datatrek #datatrek #datascience #machinelearning #statistics #deeplearning #ai

Uber Eats tackles position bias with a cutting-edge deep learning approach. Their research team recently unveiled a novel method to mitigate position bias, where users tend to favor higher-ranked stores regardless of relevance. By refining their model architecture on biased interaction data, Uber Eats effectively debiases the conversion rate to reveal true conversion probabilities. Their innovative solution involves a deep learning CVR model with a dedicated position bias side tower, enabling simultaneous estimation of True CVR and Position Bias. Careful feature selection and regularization ensure each tower operates independently, enhancing home feed recommendations and boosting user orders. Dive into my detailed video exploring these biases in recommender systems and Uber Eats' groundbreaking approach. Video Link: youtu.be/ZCO75OuMRY0   Channel Link: youtube.com/@datatrek #datatrek #datascience #machinelearning #statistics #deeplearning #ai

🚀 Attended an insightful Scaler Masterclass on “Data Science vs Machine Learning vs Artificial Intelligence”! In today’s tech-driven world, these terms are often used interchangeably — but the session brilliantly clarified how each field stands out while being deeply interconnected: 🔹 Data Science – The art of deriving insights from data through analysis, visualization, and statistics. 🔹 Machine Learning – The core engine that allows systems to learn and improve from data without explicit programming. 🔹 Artificial Intelligence – The broader vision of enabling machines to mimic human intelligence — powered by ML, deep learning, and data-driven reasoning. 💡 Key takeaway: AI is the goal, ML is the method, and Data Science is the foundation that fuels both.