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Data leakage can sabotage even the most well-intentioned machine learning models, leading to inflated results and poor generalization. In my latest article, I cover seven common mistakes in data preprocessing, feature engineering, and train-test splitting that often lead to leakage—and how to avoid them. Check it out with the free link here if you are not a Medium member yet: https://lnkd.in/gUgwt3VQ. Thank Towards Data Science for posting another article from me!

We want good-quality data but have accepted low-quality in exchange for large quantities. I discuss why we should focus on Data Quality for several reasons, including: - Every €/£/$ spent on data storage is valuable: why store data that is dropped out of analysis immediately and all the time? - Some models would require less processing power because a smaller dataset represents the larger group (**this depends on the use case). - Models have a longer re-train cycle, thus creating stability and consistency in your business processes. The ultimate reason to work towards good (not perfect) data quality is the following: Good data provides Good models, and AI is no exception. #dataquality #qualityframework #data #datasets #storage #datateam #costoptimization Design: Geoff Sence

Seven Common Causes of Data Leakage in Machine Learning Key Steps in data preprocessing, feature engineering, and train-test splitting to prevent data leakage Continue reading on Towards Data Science » https://lnkd.in/dqjVDC9D

Understanding the nuances of data-centric AI is becoming increasingly crucial. That's why we're sharing this insightful lecture by Dr. Ce Zhang, Associate Professor in Computer Science at ETH Zürich, delivered at Snorkel AI's The Future of Data-Centric AI event last year. In the lecture, Dr. Zhang delves into machine learning's cost problem, the interrelation of data and model problems, and how to discern valuable data problems from those that can needlessly consume time and resources. This talk is a must-see for anyone keen on gaining a deeper understanding of the current challenges and future directions in data-centric AI. Watch the full lecture here: #machinelearning #datacentricai #airesearch

🚀 It's Day 4 of our Machine Learning journey! Today, we dive into the ML pipeline – from data collection to deployment. Whether you're starting or refining your knowledge, this guide will show you how ML projects take shape and come to life! Read more: https://lnkd.in/dpBpiB-G #MachineLearning #DataScience #AI #GrowingTogether

🤖 New to ChatGPT for Data Analysis? Check out Boris Nikolaev's beginner-friendly guide to harnessing AI for your data projects. Read it here: https://buff.ly/4eI7WPR #DataScience #AI #ChatGPT #DataAnalysis #MachineLearning

It's not really about whether AI is replacing jobs; it's about how it can make data engineering more impactful. With AI in the mix, data engineers can ditch repetitive tasks and focus on what matters—solving complex problems and driving real business value. From automating data prep to spotting outliers, AI acts like a copilot for engineers, speeding up processes and enhancing decision-making. Learn more about its full potential: https://bit.ly/3AJNoId #AI #DataAnalytics #DataScience

🔍 Dive into the world of AI with us! Discover the fascinating duality between Prompting Engineering and Prompting Tuning. 🤖 Let's unlock the future of GenAI together! 🚀 🛠️ With Prompting Engineering, finesse is key. Guide AI's path without altering its core. 🗂️ Enrich responses with contextual prompts, like a doctor diagnosing with additional patient context. 🏥 💡 Prompting Tuning elevates AI's core abilities. 🧠 Feed extra inputs to enrich understanding, akin to giving a doctor deeper insights into illnesses. 📚 🎯 Key Differences: - Prompt Engineering for better user outputs. - Prompt Tuning hones model performance. - Prompt Engineering crafts effective inputs. - Prompt Tuning boosts knowledge in specific areas. - Prompt Engineering offers precise control. - Prompt Tuning adds depth to topics. 🌐 Resource Demands: - Prompt Engineering requires minimal resources. - Prompt Tuning can be resource-intensive. ✨ The Synergy of Both: Combine Prompting Engineering and Prompting Tuning for magical results! 🧩 Harness this dual approach to enhance model behavior and outputs, optimizing costs for the future. 🌟 Ready to unlock the potential of AI for your business? Choose the right approach for your problem and join us on this journey! 💼 #AI #Engineering #Tuning #BusinessSolutions 🔧

A great explanation about why ML models in production decay over time, explaining data drift and concept drift.

Productionising GenAI Agents: Evaluating Tool Selection with Automated Testing How to create reliable and scalable GenAI Agents for real-world applicationsContinue reading on Towards Data Science »... https://lnkd.in/eWVy33ZZ #AI #ML #Automation