Blendata的动态

Using our data lake you can retrieve information from thousands of sources and millions of data sets. And with key, relevant and interpretable data -- we make it easy for your underwriting team to cut through the clutter and reach decisions faster. Watch and learn about it here in our data lake video. #ai #ml #data #datalake

How are we still moving from Data Science thought process to GenAI thought process.

For all the Data Science/ Data Engineering enthusiasts, If you looking for a Data Orchestration tool and can't decide between Airflow, Prefect and Mage.ai, Here's an comparison blog : https://lnkd.in/gJUT5MSw Note. Do check-out Mage.ai (https://www.mage.ai/), they have built an amazing platform #DataScience #DataEngineering #AI #MachineLearning #MageAI #UnlockThePowerOfData

Madalin Stan, AI and Data Manager, delivered an engaging presentation on Navigating Data Mesh Implementations at Big Data Week. He shared best practices and practical approaches, providing invaluable insights for industry professionals. Key topics covered included: Data Mesh Principles, Core Architectural Components, Key Reasons to Choose Data Mesh, Challenges in Adopting Data Mesh. Don't miss out—watch the full presentation! ?? https://deloi.tt/3YEVLhP #BigDataWeek #AI #DataMesh #DeloitteTechnology #DataScience #GenerativeAI #TechCareers #BucharestEvents #BigData #JoinOurTeam #DataAnalytics #CareerInTech

Interested in the AI landscape in Minnesota? See what people were talking about in this brief review of the recent Data Science Initiative (DSI) Spring Research Workshop hosted by the University of Minnesota. Special shout outs to the highlights mentioned in the article: Xiao-Li MENG, Jisu Huh, @Nancy Sims (UMN), @Michael Corey (Mapping Prejudice), @Secretary Steve Simon, @Lucila Ohno-Machado (Yale), & Epic. #Data #DataScience #DataScienceMN #DSI #AI #Analytics

#AI #ML #Tech The Predictable Rise of Full-Stack Data Science: Gone are the days when we could just focus on “building the model” Continue reading on Towards Data Science ? #MachineLearning #ArtificialIntelligence #DataScience

5 PCA Visualizations You Must Try On Your Next Data Science Project Which features carry the most weight? How do original features contribute to principal components? These 5 visualization types have the…Continue reading on Towards Data Science ?... https://lnkd.in/ejW6iyP8 #AI #ML #Automation

?? Demystifying Data Science Jargon ?? Struggling with data science terminology? Here’s a quick guide: ?? Bias: Error from overly simplistic models. High bias = underfitting. ?? Variance: Sensitivity to training data. High variance = overfitting. ?? Overfitting: Model learns noise, performs well on training data but poorly on new data. ?? Regularization: Techniques like Lasso (L1) and Ridge (L2) that reduce model complexity to prevent overfitting. Let’s keep learning! ?? #DataScience #MachineLearning #AI #BigData #Learning #Bias #Variance #Overfitting #Regularization #CareerGrowth

?? 30 Days of Data Science Day 7: Mastering Data Preprocessing Now we have started Data Preprocessing Phase!!! ?? Don’t miss out—subscribe now and join me on this data-driven journey! #datascience #ai #datapreprocessing #YouTubeSeries

I'm learning new things in the ALX AiCE Essentials course. Check out this informative infographic I created with Infogram’s AI tool. The infographic outlines the key steps in the data science process, from problem definition to model deployment.